GEOGRAPHICAL DATA
Year:2019 | Volume:28 | Issue:110 |Papers Count:16

Introduction: Attitude determination of the fault planes and slid movements occurring on these planes are among the topics of interest to geoscientists. Among the methods that have been introduced to determine the attitude of the fault planes so far, the use of geological tools for justifying the geometry of the faults with surface outcrops, and examining the changes of the stress field and the displacements appeared on the Earth’ s surface can be mentioned. The slip rate is calculated using the displacement of the sedimentary rock layers relative to the displacement time and the simulation models. Materials & Methods: In this research, a geometric method is presented to calculate the slip rate of Zagros faults. We consider each fault as a continuous set of fault fragments whose surface positions are known. Given that most of the Zagros faults are hidden, locating thefaults is carried out using the geologicalmap of Iran’ s faults. The first issue in performing these calculationsisto determine the attitude of the fault planes in the Zagros seismogenic layer. The seismogenic layer is that part of the earth's crust whose deformation is elastic, and the major fractures caused by the earthquakes occur in this part. In order to determine the attitude of the fault’ sfocal plane, we use the focal coordinates of the earthquakes occurringaround each fault segment. In performing these calculations, the focal locations of the earthquakes are transferred to the geodetic coordinate system and, the equation of the fault plane is calculated using the least squares method in the Cartesian coordinate system. One can obtain the azimuth of the strike of the planes relative to the astronomical north by calculating the coefficients of the fault planes. To determine the azimuth, we first obtain the unit vector of the strike line by cross product of the geodetic z-axis (normal vector of the horizontal plane) and the normal vector of the fault plane. The fault plane azimuth will then be the angle between the strike line vector and the north vector. The north vector is the vector which is determined by connecting the point located on the center of each faultfragment to the intersection point of the horizontal plane and thez-axis. Variation in dynamic mechanisms of thefaults in the region creates fractures with different directions on the ground. We obtain theslip angle (rake) of the fault from the difference of the fault direction and the direction of thesurface fracture and the type of thefault (strike-slip, dip slip and oblique). By calculating the slip angle, we now calculate the unit vector of the slip direction from the rotation ofthe strike line vector as much asthe rake angle. Results & Discussion: In order to calculate the slip rate of each fault, we consider Zagros crust as an integrated object, which deforms uniformly by imposing the stress. Based on this assumption, we project the velocity vectors of the Zagros geodynamic network on the fault planes and calculate the slip rates using the slip direction vectors. It should be noted that the velocity vectors of the geodynamic network have been defined in the navigation coordinate system. According to the definition of the fault plane equations, it is necessary to transfer the velocity vectors to the geodetic coordinate system. The resulting slip rate is a parameter which is calculated for each fault fragmentindividually. Considering the effect of the systematic errors in the focalposition of theearthquakes, (including the error of the focal depth and the epicenter location), the slip ratesobtained for the fault fragmentsalways have systematic errors. Therefore, we define an average slip rate for each fault in order toreduce the error effect. In this study, velocity vectors of seventeen permanent stations of the Zagros geodynamic network provided by the National Cartographic Center (NCC) are used. The focal positions of the earthquakes are also published by the International Institute of Earthquake Engineering and Seismology (IIEES). Conclusion: The obtained results showed that the regions with high fault slip rate usually have dense earthquakes. In addition, the seismicity potential of any region can be found by comparing the slip rate of each fault and the density of its earthquakes in the region. According to the changes in the slip rate obtained in Zagros, faults in the western part of Zagros, especially in Ilam province, have low slip rates. However, the province is considered asone of the seismic areas of the state in terms of earthquake density. It means that most of the slip movements occurringon the faults of the western region have been accompanied by vibration.

Introduction: In the past few decades, urban environments have expanded much larger than before. One of the most important problems in most metropolises and even small cities is the management of the transportation system. An advanced monitoring system of urban vehicles allows for overcoming the traffic problems. With the development of unmanned aerial vehicles (UAVs), continuous and accurate monitoring of urban environments has been provided for the users. In this research, an efficient method is presented that detects the vehicle in the UAV images. The proposed method is effective in terms of computational speed and accuracy. Materials and Methods: The foundation of the proposed method is based on the characteristics of the local features in the UAV images. The presented method consists of two main stages of training classification model and detecting vehicles. In the first part, local features are extracted and described by the SIFT algorithm. The SIFT algorithm is one of the most powerful algorithms for extracting and describing local features that are used in various photogrammetry and machine vision applications. This algorithm is robust to geometric and radiometric changes of the images. Due to the high dimensions of extracted features from all the training samples, the BOVW (Bag of Visual Word) model has been applied. This model is used to reduce the dimensions of the features and display the images. Simple and efficient computing is one of the significant features of the BOVW model. At this stage, after producing a library of features, the SVM classification model is trained. In the detection part of the algorithm, the images are entered into the algorithm and the local features are extracted in all images by the SIFT algorithm. The BOVW model is often used to display an image patch. In most researches, this model is implemented by applying a search window to the whole image. This type of methods has a higher confidence level in detection, but it is a very time consuming process and increases the volume of the computations. For this purpose, the approach of points clustering and their representation by the BOVW model is proposed. In this method, features that are within a certain range are considered as a cluster. Euclidean distance is used in image space for clustering. Then, the clusters produced by the BOVW model are displayed. Then, a feature vector is constructed for each cluster. The trained SVM is applied to each of the production vectors and each cluster is classified as a vehicle and non-vehicle. If the cluster is detected as a car, the position of the center of that cluster is marked on the image. Results and discussion: The proposed method was implemented on 8 images with a number of different car targets. Also, considering the use of the search window approach in many researches, our results were compared with the results obtained by other researchers. The results show that the calculation time of the proposed method is 82 seconds, while the search window method takes 2496 seconds to run. In order to verify the accuracy of the algorithm, two criteria were used. The first criterion is the “ Producer's accuracy” , which represents the proportion of correct detections of the vehicle to the entire vehicles existing in the images. This criterion is 75. 79% for the proposed method. The second criterion is the “ User's accuracy” . This criterion is obtained by dividing the correctly detected samples into the sum of the correctly and incorrectly detected samples. The User's accuracy criterion has been reported to be 59. 50%. Conclusion: The value of the Producer's accuracy criterion is greater for the search window method which has led to a more accurate detection of vehicles compared to our method. This is due to the small moving steps of the search window in the entire image. However, the search window method has increased the amount of the time spent on the calculations. The User's accuracy criterion shows that the proposed method has less incorrect detections. The results indicate that our method has a higher degree of reliability. The average of these two criteria indicates the superiority of the proposed method in terms of the accuracy of the calculations. On the other hand, the proposed algorithm has a great advantage in terms of computational speed compared to the search window method.

Introduction: Soil salinity is considered to be a major cause of desertification and destruction of environmental resources in arid and semi-arid regions. Due to the importance of conserving natural resources and also the increasing trend of soil salinity during the last few years, determining the extent of salinity spread and its severity in affected areas is especially important. Using the potential of high resolution satellite imagery and remote sensing techniques is one of the most effective ways for detecting salinity in salt affected regions. Among different satellite sensors, satellites which provide large scale multispectral satellite imageries with high spatial and spectral resolution have a high potential for assessing salinization and mapping soil salinity in study regions. Materials and Methods: Accordingly, this paper aims to map different salinity levels in an area in Kuh-Sefid district (Qom Province), which is highly affected by salinity, using Sentinel-2 recent imageries. For this purpose, field study was conducted and salinity level was measured for several soil samples randomly collected from the site. Different salinity indicators, like salinity and vegetation indices, LST, and Digital Elevation Model of the site produced based on SRTM elevation data were also extracted from corresponding satellite images. These indicators were then used for mapping salinity levels in Kuh-Sefid district. Principal Component Analysis was used to gain the largest amount of available information and reduce the dimensionality of data cube. Based on the performed analysis, different supervised classification algorithms were used to map salinity levels and divide the site into five distinct salinity classes-normal, slightly saline, moderately saline, highly saline, and extremely saline. Results and Discussion: Data was analyzed based on five supervised classification algorithms, including Minimum Distance, Mahalanobis, Parallelepiped, Maximum Likelihood, and Support Vector Machine (SVM). Results indicated that the best accuracy in mapping salinity classes was obtained from SVM classifier, with overall accuracy of 92. 218 and Kappa coefficient of 0. 894. The results also revealed that Maximum Likelihood Classifier with overall accuracy of 90. 718 has a high potential for discriminating saline surfaces and producing salinity map. In addition, more than 62% of soil types in this region are categorized in moderate, high and extreme salinity classes, which indicates that the area is highly at risk. Conclusions: Evaluating the results of salinity classes shows that the eastern areas of Kuh-Sefid are relatively more severely affected by salinity. This is due to vicinity of Qom Salt Lake and drawing of saline soil into surrounding areas. If this process continues, it will lead to loss of soil fertility and crop productivity in this region over the next few years. Due to their potential in detecting soil salinity and providing large-scale maps of salinity levels, multispectral Sentinel-2 imageries are considered to be a powerful tool in soil reclamation programs and land management studies.

Introduction: Persistent Scatterer Interferometry (PSI) is a technique for detection and analysis of a network of coherent pixels referred to as the Permanent/Persistent scatterer (PS) which have high phase strength over long time periods. This technique has been widely used by the scientific community to measure the displacement related to thesubsidence/uplift, landslide, tectonic, and volcanoes. As the density and quality of PS pixels are important factors in PSI algorithms, the concept of polarimetric optimization in the PSI algorithms was proposed to improve the number of PS pixels. The recent launch of radar sensors operating with a polarimetric configuration can help improvingthePS-InSAR analysisby increasing the PS density. Therefore, the combination of thepolarimetric and interferometric techniques helpsimprove the PSI techniques, especially in non-urban areas which suffer from lack of the PS density. In this study, we investigated how the contribution of the S1A and TSX data in the PSI analysis could lead to the improvement of the results of the PSInSAR algorithm. Indeed, the main objective of this paper is to illustrate the capability of each dataset for improving the polarimetric optimization results. 1. Materials & Methods: 2. 1 The proposed method was tested using a dataset of 40 dual-pol SAR data (VV/VH) acquired by Sentinel1-A between February 2017 and May 2018 and 20 dual-pol SAR data (HH/VV) acquired by TerraSAR-X betweenJuly 2013 and April 2014. 2. 2 Polarimetric SAR Interferometry The general principle of polarimetric SAR interferometry was proposed by (Cloude & Papathanassiou, 1997) for the first time. The scattering matrix S represents the polarimetric information associated with each pixel of the image. Considering the monostatic configuration, the scattering matrix S is defined as follows: (1) Where and are co-polar channels, is the cross polar channel. This matrix can be represented with the target scattering vector as: (2) Where, is the transposed operator. The Pauli vector for the dual-pol data (HH/VV) of the TerraSAR-X sensor, is written as: (3) Similarly, the Pauli vectorfor the dual-pol data (VV/VH) of theSentinel1-A sensorcan be expressed as: (4) In order to generate scattering coefficient μ , projecting the scatteringvector on the projection vectorwould be sufficient: (5) Where is thelinear combination of the elements of matrix S, i is the correspondent of the 2 images, and * represents the conjugate operator. The projection vectorfor the dual-pol data isdefined as: (6) Where, and are two real parameters whose ranges are finite and known and are related to the geometrical and electromagnetic properties of the targets. In our research, the main purpose of the polarimetric optimization is to find theoptimum projection vector, in a 2-dimensional search space, and 2. 3 Amplitude Dispersion Index Optimization Substituting (5) into (7), the ADIfor the polarimetric case () can be expressed as follows: (7) (8) According to (6), the polarimetric optimization problem isreduced to finding a suitable and in a finite and known range, so that (8) is minimized. 2. Results & Discussion: The results showed that the proposed method improved the performance of the PSInSAR algorithm in two terms of phase quality and density of the PS pixels. Compared with the VV channel, , the number of PSC and PS pixels increased about 2 and 1. 7 times In S1A data, using the ESPO method while, compared with the normal channels like HH and VV, the number of PSC and PS pixels in ESPO method increased about 3. 5 and 3 times in TSX data. Based on these results, the optimization methods are more effective in improving the quality of the PSC densitythan in increasing the number of PS pixels. This is mainly because the employed optimization is based on minimizing ADI criterion which is used in the PSC selection. Moreover, ESPO method has been more successful for TSX data compared to the S1A data. This result is due to the higher capability of the TSX data in creating more diverse scattering mechanisms and hence identifying more optimum scattering mechanism compared to S1A data. We also investigated the effect of polarimetric optimization in increasing the PS density in urban and non-urban areas. The experimental results showed that the method succeeded to significantly increase the final set of PS pixels in both urban and nonurban areas. 3. Conclusion: The results show that the optimization methods have been more successful in the improvement of PS density for the TSX data compared to the S1A data. This result is due to the higher capability of the TSX data in creating more diverse scattering mechanisms compared to the S1A data. In summary, thanks to the polarimetric data, it is possible to exploit a larger number of pixels compared with the single polarization case.

Introduction: Oak is a common species in Iran and the most important one in Zagros forests. Zagros forests play a crucial and effective role in water supply, soil conservation and climate modification in Iran. Unfortunately, a significant part of those forests suffer from oak decline. Oak decline (or oak mortality) is a widespread phenomenon in oak forests around the world, which has gained the attention of many researchers in forestry over the past decade. In Iran, this phenomenon was first observed in Zagros forests in 2013. Factors affecting oak decline and their mutual interactions are not clearly identified, which makes understanding and modeling of these processes challenging. Only a few studies have been performed in relation to this phenomenon in Iran. Thus, we chose to determine the most effective parameters and find the best modeling method for oak decline in Iran and especially in Lorestan province. Materials & Methods: In order to find effective environmental variables, related literature review was thoroughly investigated. Environmental parameters including temperature, precipitation, elevation, slope, direction, soil type, and amount of aerosols were selected as basic influencing parameters. All parameters were then interpolated to produce raster data with 30-meter cell resolution. To find the optimal combination of the parameters, four operators including multiplication, logarithm, hyperbolic transformations, and principal component analysis (PCA) were used. A total 385 different combinations of the influencing parameters were produced using the above mentioned operators. The relation and weight of each parameter are unknown, thus Artificial Neural Networks were used to model oak decline process. Three feed forward artificial neural network, including Back-propagation Neural Network (BP), Probabilistic neural network (PNN) and Support Vector Neural Network (SVNN) were selected as modeling methods. Then, 385 different combinations of the influencing parameters were used in the above mentioned models. To train and evaluate each neural network, a total number of 10000 samples were randomly selected from the study area. 70 percent of these random samples were used to train, 15 percent to evaluate and 15 percent to validate the models. Also, cross-validation method was used to avoid over fitting of neural networks. Finally, 1155 created NN models were compared using R parameter to find the best configuration for modeling oak decline and identifying the most influential environmental parameters in oak decline. Results & Discussion: Evaluating 1155 different networks indicated that Probabilistic neural network (R=0. 87) with 6 inputs, including 1) elevation, 2) slope, 3) direction, 4) aerosols, 5) soil type and 6) principal component of temperature and precipitation, performed better than SVNN and BP in modeling oak decline. Moreover, using different combinations of influencing factors improved the results and increased correlation coefficient (R) of optimal inputs by 0. 05 as compared to initial inputs. Thus, it can be concluded that increased number of inputs does not necessarily guarantee a better performance. Furthermore, two principle parameters of temperature and perception have a more significant role in modelling drought stress as compared to other parameters. Conclusion: Oak decline is a complicated phenomenon and different factors contribute to its occurrence. The present study investigates all environmental parameters affecting oak decline through a comprehensive literature review. Results indicate appropriate performance of probabilistic neural networks in modeling oak decline. Moreover, principal component analysis is considered to be a useful tool for modeling of drought stress in oak trees. Due to different accuracy and precision of these neural networks, it is necessary to evaluate different configurations. For further researches, it is suggested to use other parameters, such as distance from population centers, water table, age of oak trees, oak tree height and characteristics of other nearby trees.

Introduction: Rain is one of the most important atmospheric phenomena affecting human life. Rainfall prediction is important for various purposes such as planning for agricultural activities, forecasting floods, monitoring drought and providing resources for consumable water. The rapid expansion of using artificial neural networks (ANNs) as an experimental and efficient model in various sciences including meteorology and climatology implies the high value of studying these types of models. Materials and methods: The purpose of this paper is to predict the monthly rainfall in Iran, using the combination of artificial neural networks and extendedKalman filter. For this purpose, the monthly average rainfall data of about 180 synoptic stations spreading across the country were used during the years 1951 to 2016, then, the monthly rainfall was predicted for the year 2017 using the article’ s method. Artificial neural networks are a method for the approximation of the functions and prediction of the future state of various systems. Artificial neural networks discover the law latent in them and transfer it into the network by processing the experimental data. The smallest processing unit of information in the artificial neural network is neuron that builds the bases for the application of neural networks. Each neural network consists of a number of nodes which are the neurons, and the communication weights that connect the nodes together. Input data is multiplied by their corresponding weights, and their sum is entered into the neurons. Each neuron has a transfer function. This input data passes through the transfer function and specifies the output value of the neuron. The back propagation algorithm is one of the most commonly used algorithms for teaching these networks, but the back propagation algorithm depends on the selection of the number of hidden neurons. Also, the convergence speed of the back propagation algorithm is very slow comparing with the proposed method in this paper, and is very sensitive to the noises present in the input and output data set, which is used for teaching the neural network. In addition, it has a poor performance in modeling the complex processes. One of the most famous methods to eliminate the aforementioned defects is the use of the Kalman filter. The Kalman filter contains a set of mathematical equations that performs a repeated process, prediction and updates, and is also an optimal tool in minimizing the covarianceof the estimated error. The leading neural network can be considered as a nonlinear dynamic system with synaptic weights and equate the teaching of the neural network with the problem of estimating the state of the nonlinear system. Therefore, the extended version of the Kalman filter can be used to estimate the adjustable parameters of the artificial neural network like the neural network weights. Results and discussion: The climatic zonation of Iran was used for the data separation by the method of Coupon-Geiger which has been conducted by other researchers, and Iran was divided into eight climatic zones. This zonation divides Iran into dry and hot desert, dry and cold desert, dry and hot semi-desert, dry and cold semi-desert, moderate with dry and hot summers, rainy moderate with warm summers, snowy with dry and hotarm summers, snowy with dry and warm summers climates. This artificial neural network which has been taught by the extended Kalmanfilter, was used for the prediction in each of the climatic zones. A multi-layered artificial neural network with two hidden layers has been used. There are 10 neurons in each of the hidden layers, and 7 neurons in the input layer, which are the same numbers as the network inputs. The methodology is that the year and number of months, the average monthly temperature, the average monthly wind speed and the geographic location of the synoptic stations are considered as the input parameters, and the average monthly precipitation as the output parameter. The difference between the observed and the predicted values of the monthly precipitation in 2017 was calculated for all stations and was considered as an error. The Root Mean Square Error (RMSE) of these differences was calculated for the 8 climatic zones. The RMSE is lower for dry and hot desert climate than for dry and cold desert climate. This RMSE is lower for dry and cold semi-desert climate than for dry and hot semi-desert climate. The RMSE is lower for moderate climate with dry and hot summers than for moderate rainy climate with warm summers. The RMSE is lower for snowy climate with dry and hotsummers than for snowy climates with dry and warm summers. Conclusion: In most cases, the RMSE for hot climates is less than others that represents the efficiency of the proposed method for the prediction of monthly precipitation in hot climates.

Introduction: Heat island phenomenon occurs when the land surface temperature and the air temperature in urban areas are higher than that of the surrounding areas. This temperature difference is shown as the urban heat islands on thermal maps. Information obtained from the urban heat islands can be a useful source in urban planning applications. The availability of reliable information about the urban heat islands plays an important role in predicting and preventing the occurrence of many heating risks in urban areas. One of the common methods of calculating heat islands intensity in urban areas is the use of two temperature sensors installed in the city and around it. Given the limited temperaturemeasuring stations, there is no accurate estimate of the urban heat islands. With the introduction of Remote Sensing technology into the space arena, and with the help of satellite images processing, a precise map can be produced for the land surface temperature, i. e. a precise estimation of the urban heat islands is obtained by calculating the pixels temperature difference at the urban areas and around them. Therefore, one of the important issues in such studies is to detect the urban and non-urban pixels and to separate them from each other. Materials&Methods: The most important reason for the occurrence of the heat island phenomenon is the change in land use from rural to urban, which is well exhibited in the urban cover index maps. In this paper, in order to measurethe intensity of surface urban heat islands, a method based on generating the urban percentage map was proposed by combining the Land Surface Temperature (LST) map, the Normalized Difference Built-up Index (NDBI) map and the Normalized Difference Vegetation Index (NDVI) map. Considering the relationship between the land surface temperature and the land cover type, it can be said that the relationship between the land surface temperature and the urban percentage map follows a linear function which can be fitted to the land surface temperature graph in terms of land cover type. Finally, the Urban Heat Island Intensity (UHII) map was calculatedfrom the slope of the fitted line. In order to evaluate the strengths and weaknesses of the proposed method, a classification-based method was used to separate the urban and non-urban pixels and to calculate the urban heat island intensity. The proposed method was implemented on the Landsat-7 ETM + satellite data in the city of Rasht and on the Landsat-8 OLI / TIR satellite data in the city of Langroud. Results&Discussion: The results of the classification-based method indicated a large difference between the maximum and the minimum temperature of the urban areas, which led to a high-temperature changein all land cover typesin the study area. Therefore, the use of the average temperature of each class to calculate the heat island intensity is not a suitable method and the accuracy of the heat islands maps is not high and they cannot be used in applications that require high precision. Although, this problem can be solved by increasing the number of classes, increasing the number of classes requires more training data and a sensor with higher spatial resolution. By contrast, the results indicated that the proposed method (based on the urban percentage map) had a high accuracy for calculating the urban heat island intensity which was similar for both study areas. Also, fitting a linear function to the values of land surface temperature and the urban percentage map led to decreasing the effect of suspicious pixels (noisy pixels) on the overall accuracy of the estimation of the urban heat island intensity. Meanwhile, the results obtained on two datasets indicated that this method did not require any training data or any other background information about the study area and it can be applied for many satellite images having thermal band with any spatial resolution. However, because of the ineffectiveness of urban cover indicators in desert areas, the heat islands intensity in these regions was underestimated. Conclusion: In applications that do not require high accuracy in calculating the urban heat island intensity, and there are high spatial resolution satellite imagery and sufficient training data in a region, the use of a classification-based approach seems to be suitable. Since the collection of such data and information is costly, a new method based on the urban percentage map was proposed in this paper by fitting a line to the LST parameter diagram in terms of the NDBI index for measuring the heat island intensity. The results indicated the higher efficiency and accuracy of the proposed method compared to the conventional classification-based methods for calculating the urban heat island intensity.

Introduction: Cities have always been under the influence of various factors and developed under such conditions. Countries around the world are increasingly moving toward urbanization. Physical development of cities occurs in the form of human activities or changes in urban (or rural) land use, and lead to widespread use of lands and adverse environmental effects. In some cases, urban growth leads to environmental hazards and threats human societies. Although the effects of natural factors such as geomorphological phenomena have not been scientifically considered in the development of the study area, there factors had a leading role in this development. Due to geomorphological situation, elevations and steep areas, scattered fault lines and rivers full of water, development of human settlements in the study area faces many constraints. Therefore, it is necessary to plan urban development in the study area based on the geomorphological situation of the region. Accordingly, the present study seeks to evaluate the trend of changes occurred from 1992 to 2017 in the residential districts of Marivan. It also aims to determine the extent of urban growth towards areas facing geomorphological hazards, and finally to predict this trend for 2035. Materials and Methods: The present study takes advantage of an analytical and statistical research method, along with the necessary software. Moreover, it seeks to study the trend of urban development from 1992 to 2017, and also predict the future trend of development for 2035. Thus, satellite images received in June 1992, 2001, 2011, and 2017 are collected. After preprocessing the images, a land use map is extracted based on the situation of the study area in 1992, 2001. 2011 and 2017. Then, based on these maps and using effective variables, a map is produced based on the predictions made for the residential areas in 2035 by LCM model. Modeling and prediction are performed using LCM model in four steps: 1. Examination of Land Use Changes; 2. Mapping Potential Transfer using Markov Chain. 3. Extracting a predictive map. 4. Evaluating the accuracy of prediction. After predicting and extracting a map of residential areas for each time period, distribution of geomorphologic hazards in these areas is evaluated. In fact, development trend of high risk residential areas has been evaluated. Discussion and Results: A large part of the study area is mountainous, and these elevations have somehow limited the development of human settlements. Since the present study seeks to determine the trend of human settlements development in areas facing geomorphological hazards, a map has been extracted for these prohibited areas before evaluating the trend of development. These prohibited areas have been mapped in order to identify hazardous areas, and to evaluate development of residential settlements toward these areas. To prepare this map, multiple criteria have been selected based on the situation in the region and experts’ opinion. Then in accordance with the purpose of this research, an information layer was produced using these criteria. Regarding geomorphology, regions with an altitude of more than 1700 m, slopes of more than 30%, north-south direction of the slope, area within 1000 m radii around fault lines and within 200 m radii around rivers are referred to as prohibited areas. After determining prohibited areas, human settlements in the study area were mapped based on 1992, 2001, 2011, and 2017 information. Then, trend of settlement development in prohibited areas was estimated and projected for 2035. Conclusion: Based on the evaluation of results, there is an increasing demographic trend from 1992 to 2017, so that residential area has increased from 7. 8 km in 1992 to 10. 9 km in 2017. Maximum development occurred from 2001 to 2011. During this period, settlements developed 3. 6 km2 and reached around 14. 5 km2 in 2011. From 2011 to 2017, settlements area reached 16. 6 km2. Apart from the increasing trend of development in residential areas during these years, this development has mostly occurred toward hazardous areas. So that in 1992, around 1. 7 km2 of total residential area was located in prohibited areas, most of which included steeped areas and rivers’ border lines. In 2001 and 2011, this trend has increased from 2. 3 to 2. 9 km2, and reached 3. 3 km2 in 2017. Considering the increasing trend of population toward Marivan, increased constructions in peri-urban and rural areas of Marivan and also along the main road of this city, development of settlements toward prohibited areas has mostly occurred in these areas. According to the main purpose of the present research, development of residential areas is projected for 2035 based on land use in pre-specified years. Results indicate that total area of settlements will increase to about 24. 3 km2 in 2035, about 5. 7 km2 of which will be in prohibited areas.

Introduction: Ionosphere is a region of ionized plasma that extends at an altitude of 80 to 1, 200 km above the earth's surface. The ionosphere consists of free electrons and ions formed during the ionization process. Total electron content (TEC) in the ionosphere is reported in TECU units. Each TECU is equivalent to 1016 electron units per square meter. Ionosphere is highly sensitive to any atmospheric turbulence, and thus is considered to be an atmospheric event sensor. The present study seeks to investigate the effect of space and temperature on the amount of total ionospheric electron content in order to accurately estimate TEC value. To reach this aim, variations in latitude and longitude are decomposed for a given period of time using the process of transforming wavelet to frequency component and modeled using a variety of artificial neural networks. Materials and Methods: Here, after separating the location and temperature parameters in each region, ionospheric electron density is estimated for each spatial and temperature parameter separately and also as a combination using the capabilities of artificial neural networks and wavelet transform. TEC value for each location and temperature parameter is extracted from the ionospheric maps and then used as input data in the suggested method. These maps show ionospheric electron content. The standard format of ionospheric maps, which contains TEC values is called IONEX. These files are received from the website of Iranian National Mapping Agency. Results and discussion: In general, IONEX is divided into three different parts: description, TEC maps, and standard deviations of maps. TEC values are presented in a regular network. Each IONEX file includes 25 maps, the last of which is the first map of the next day. As mentioned before, TEC value gives us a better understanding of ionospheric behavior. Availability of enough data and time coverage are two important factors in understanding a phenomenon and proper evaluation of its behavior. Conclusion: As results of artificial neural networks indicate, MLP generally has lower RMSE values. Therefore, it gives a more accurate estimation of TEC, compared to other artificial neural networks. Also compared to artificial neural networks, a combination of artificial neural networks and wavelet shows better results. The best condition of all three methods shows that compared to other methods, temperature variations give us a better estimation of TEC in ionosphere.

Introduction: Groundwater, its quantitative/qualitative variables, and variability directly affect human life, and thus has always been one of the major topics in scientific and academic research. Due to geographical, climatic and hydrological conditions, and specific patterns of surface water and subsurface water resources exploitation, this country has always faced water scarcity. As a result of global and regional changes in temporal and spatial patterns of rainfall, this has intensified in recent years. Therefore, exploitation of groundwater resources has been considered as an option for supplying agricultural, industrial and drinking water. However, excessive exploitation of these resources will result in their destruction. In recent years, excessive removal of groundwater and reduction of groundwater levels have resulted in some problems like subsidence in some plains. This makes it necessary to study the quantitative and qualitative changes of these resources more clearly. Due to the complex nature of aquifers’ hydrogeological systems, accurate investigation of these resources seems costly and even impossible. Thus in order to achieve a better understanding, it is necessary to use different methods for estimation and evaluation of such variables. Material & Methods: Most environmental features are completely continuous in nature, which makes it impossible to measure these features in every part of these environments. Thus, we can generalize measured samples to other areas lacking accurate measurements, and in this way estimate these variables in unmeasured areas. This is also true about quantitative and qualitative variables of groundwater, i. e. by collecting samples from some sections, we can measure different characteristics in these samples. This surface modelling-or in other words, generalization of points to surface-can be achieved with mathematical and statistical relationships and rules. Due to the spatial structure of the measured specimens, geo statistics is used in this regard. In recent years, artificial intelligence models, inspired by the natural nervous system and simulating its function, have yielded a very satisfactory result in groundwater estimation and studies. In order to evaluate the accuracy of geo statistical methods and artificial neural networks, the present study takes advantage of statistics and measurements collected from groundwater level of 46 wells in Shabestar-Sufiyan plain in 2014. Kriging method (geo statistics) and multilayer perceptron neural network method (MLP) were used along with error propagation pattern (BP) to estimate unmeasured features in the study area. MATLAB 2016B was used to perform the neural network modeling and ARCGIS10. 5 was used to perform Kriging method and prepare the final maps. In both neural network and kriging models, geographical coordinates of observed wells was used as input and measured water table was introduced as the study goal. Primary data reduces the accuracy of models. Thus, data was normalized before being introduced to the neural network model. After the initial analysis of data dispersion and normalization, logarithmic transfer function was used due to the relative improvement of data in Kriging estimator model. Results & Discussion: Results indicate that at the training and testing stage (with Sigmoid tangent activation function (Tansig) and 9 neurons in the middle layer), neural network method (MLP) with a high correlation coefficient (0. 96) and root mean square error of 13. 18 is more accurate than Kriging method with J-shaped Variogram model, a correlation coefficient of 0. 90 and root mean square error of 20. 10. Due to realistic results provided by neural network method, it is considered to be a more efficient method in estimation of water table in Shabestar-Sufiyan Plain. This is also consistent with earlier hydrogeological studies (regarding aquifers) performed on the ability and flexibility of Artificial Intelligence models. Conclusion: Results obtained from the current research, and previous studies conducted in this field indicate that most artificial intelligence computing models are capable of evaluating and estimating continuous environmental variables. On the other hand, understanding groundwater resources’ conditions is considered to be crucial. Thus, new methods, such as artificial neural networks (ANN) and adaptive neuro-fuzzy inference methods (ANFIS) and fuzzy inference systems (FIS), which provide greater accuracy can help decision makers and researchers in maintenance and improvement of the groundwater status.

Introduction: Natural disasters have always been considered to be a great challengefor sustainable development throughout the world. Consequently, the paths to this development through the vulnerability reduction patterns are very important. Therefore, it is particularly important to reduce the risks of these disasters and necessary to consider a proper position in the national policy-making of countries in order to provide an appropriate condition for the effective reduction of the risks in different levels. Most of the plans made in the field of earthquake management are limited to the time interval during and after the occurrence of the crisis and less attention is paid to the pre-disaster planning. Among the plans for the risk reduction, resilience can be considered a more accurate and successful plan due to its consideration of social, institutional, economic, and physical aspects of a city. In fact, it aims to reduce the vulnerability of the communities and prepare people to face the risks caused by natural disasters. The management of natural disasters requires understanding their nature, accurate assessments, planning and finally providing proper strategies. Hence, it is very important to explain the relationship between resilience in natural disasters (such as earthquake) and reducetheir impact given the results that it might have and the emphasis of this analysis on the aspect of resilience. Materials & Methods: The present study is an applied study in terms of purpose and is adescriptive survey type in terms of research method. Documentary method based on library studies and survey approach with a questionnaire tool was used to collect the research data. The assessment criteria for the resilience of urban communities were first determined in the present study. Then, a questionnaire was designed and distributed among the residents of Nourabad and Maskan-e Mehr in order to prepare the initial matrix for these criteria. The study population consists of the residents of Nourabad and Maskan-e Mehr of this city. Cochran's formula was used to estimate the sample size. According to the initial results of the census conducted in 2016, the population of Nourabad, including the residents of Maskan-e Mehr, was 66417. Therefore, given this population, the sample size was obtained to be 384 for the city of Nourabad using Cochran’ s formula and the sample size for Maskan-e Mehr was obtained to be 500 households with household dimension of 5. 5, given the number of households settled in Maskan-e Mehr until the end of 2017. The sample size was estimated to be 340 people for Maskan-e Mehr using Morgan’ s table, . The scoring basis of the criteria was based on Likert 5-point scale with1 representing very low, 2 low, 3 medium, 4 high, and 5 very high. Finally, the average point of this questionnaire was considered as the initial matrix for VIKOR model. In the proposed method, the final weight of the criteria was determined based on AHP pair-wise comparison matrix. Finally, the criteria were ranked based on VIKOR technique procedure. In general, the findings of the current research were analyzed through hierarchy analysis and integration of the indices using VIKOR technique. Results & Discussion: In the first step, the raw data of each criterion associated with the resilience of Nourabad County and Maskan-e Mehr, which were extracted from the questionnaire, were used and the decision-making matrix was created. In the second step, Equation (1) was used to obtain the weight normalization matrix for Nourabad and Maskan-e Mehr. In the third step, AHP method was used for the weighting of the normalized matrix and determining the weight of the indices. The weights of the proposed indices were determined by the residents of Nourabad County and Maskan-e Mehr and were calculated using the AHP method in Excel 2013 software and were assigned to each index. After determining the weight of the criteria, the values of the normalized matrix for each option was multiplied by the weight of the criteria and consequently, the weighted normalized matrix was obtained. In order to determine the best and worst values for the criteria, equations (2) and (3) i. e. determining the positive and negative ideal points were used. Equations (4) and (5) were used to calculate the distance of the options from the ideal solution. Finally, VIKOR index (Qi) was used to rate the resilience of Nourabad County and Maskan-e Mehr based on the distance from the ideal solution. Generally, the views of the residents of Nourabad and Maskan-e Mehr were combined through VIKOR method to determine the value and importance of the criteria and the final weights of the criteria were determined using the AHP method. Applying the obtained weight on the initial values of the criteria and combining the weight indices, Nourabad County and Maskan-e Mehr were prioritized in terms of resilience. Conclusion: The results obtained from VIKOR technique showed that this method, as one of the multi-criteria decision-making method, has capabilities including multi-attribute utility theory or non-ranking methods. On this basis and after calculating the weights through hierarchy analysis process and using VIKOR technique, the difference in the resilience of Nourabad County and Nourabad Maskan-e Mehr was determined. Based on the calculations and the associated indices, Nourabad County has the highest resilience level with S=0. 763, R=0. 49, and Q=0. 966, whilethe Maskan-e Mehr of this city has the lowest resilience with indices S=0. 666, R=0. 272, and Q=0. 626. Given the Q index, Nourabad County (pre-created communities) has a more favorable condition in terms of resilience against natural disasters (earthquakes) compared to the Maskan-e Mehr of this city (planned communities) in social, institutional, economic, and physical aspects.

Introduction: With recent advances in satellite remote sensing productions in past few decades, several indices have been provided for the study of vegetation dynamics, and especially for the assessment of drought impacts. Among these, two vegetation indices-Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)-have gained the attention of various researchers. Therefore, the present study aims to investigate the reaction of these two vegetation indices (i. e. NDVI and EVI) to dry and wet years in a dry plain in Iran (i. e. Sistan plain in eastern Iran). Materials & Methods: To assess the sensitivity of these indices to dry and wet years, two different databases were required. First, NDVI and EVI image base received from Terra satellite (MODIS sensor) for April, May and June 2000-2014, and downloaded from EOS website. Second, daily data base of Zabol synoptic meteorological station (for a statistical period of 30-years 1985-2014) received from Iran Meteorological Organization. After data acquisition, separate vegetation dynamics maps (for April, May and June) were produced for the study area based on the information derived through processing of MODIS sensor images (Terra satellite) using NDVI and EVI. Effective drought index (EDI) was used to determine the frequency of dry and wet years in Sistan plain. Results & Discussion: Mapping of vegetation dynamics based on images received from MODIS sensor (Terra satellite) for a 15-year statistical period (2000 to 2014: April, May, and June) indicated that NDVI and EVI had significant differences in exhibiting the dynamics of vegetation in the study area. These differences were obvious in areas with average amount of vegetation (0. 4-0. 5 in both NDVI and EVI) and also in areas with sparse dispersed vegetation (0. 3-0. 4 in both NDVI and EVI). In average levels of vegetation, total area of vegetation calculated by EVI is much higher than what is calculated by NDVI, while in sparse and dispersed vegetation, total area of vegetation calculated by NDVI is almost higher than EVI. Subsequently by selection of a dry (2010-2011) and a wet year (2005-2006), we compared changes in total area of vegetation (average and sparse) calculated by NDVI and EVI. Regarding the response of these two indices to dry and wet years, it was concluded that NDVI shows a better and more logical response during droughts, while EVI provides better results in wet years. However, it should be noted that the mean annual precipitation of Sistan plain is so low (59 mm per year) and its evapotranspiration is so high (4800 mm per year) that precipitation does not play a significant role in vegetation dynamics of this plain. Therefore, water flow in Helmand River, which is the lifeblood of this desert, is much more important than this limited precipitation in Sistan plain; hence, we can conclude that meteorological drought monitoring indices cannot reflect the relationship between drought and vegetation dynamics in Sistan plain, and this makes it difficult to compare NDVI and EVI in the region. Conclusion: In general, it can be concluded that NDVI is a more suitable index for dynamics of vegetation in plains such as Sistan, whose life depends not on precipitation but on water running in the river. Because of the computational nature of EVI, it responds better in areas with dense vegetation. According to the vegetation type obtained from MODIS sensor images and field visits, NDVI is a better index for these types of plains.

Introduction: Like every other human activity, military activities are also affected by weather conditions. From a military perspective, climate studies are very important. In wartime, air and topography are more influential than any other physical factor, such as weapons, equipment, and logistics. These factors have been somehow effective in most victories and defeats. Therefore, military forces need special equipment, special training and adaptation to environmental conditions. Military commanders pay special attention to the daily mean minimum and maximum temperature, as well as very high or very low temperature. Troops usually need two weeks to adapt to climate conditions, but in extreme heat they may never reach their full efficiency. The importance of climatology in military plans is summarized in two stages: first, a preliminary stage in which time and location for the establishment of bases and deployment of military equipment are predicted, and the second stage in which atmospheric phenomena are linked and connected with the planed military operations. Accurate understanding of climate elements and their effects can lead to the discovery of positive and negative points and, as a result, a better planning for the promotion of military operations. Therefore, researchers have always been interested in climate parameters and investigating the effects of these parameters on defense, military, and passive defense discussions have always been inevitable. Materials and Methods: After performing secondary research and reviewing different resources, the most important climate parameters affecting the performance of military forces were identified and the initial database was formed. Data was received from Iran Meteorological Organization for 21 weather stations in Khuzestan province regarding a period of 25 years (1988-2013). Then, database was compiled using the received data and the geographic features of the stations (longitude, latitude and elevation). These parameters include: Long-term average annual rainfall, average number of dusty days in each year, mean maximum temperature, long-term average of wind speed, wind direction, long-term average of sunshine duration, average humidity In order to map climate parameters, each parameter-related layer was prepared using interpolation method and IDW model. Then, each layer was weighted using decision-making process. Afterward, each layer was classified into 5 categories, each of which was weighed according to its importance. In the next step, the fuzzy TOPSIS model was used to analyze collected data. Results and discussion: Investigating the characteristics of precipitation layer and the weight of each category proved that the highest weight is related to the 670-570 category and the lowest weight is related to the 200-100 mm category. The highest level of precipitation (670-570 mm category) has occurred in Dehdez and Izeh, and the lowest level of precipitation (200-100 mm category) has occurred in Bostan, Hanijan, Mahshahr, Shadegan and Abadan stations. The maximum temperature layer in Khuzestan province showed that the highest temperature (44-48 ° C) had a weight of 4. 52, while the lowest temperature in 19-26 ° C category had a weight of 0. 73. Gotvand and Ahwaz agricultural research station have the highest wind speed (7. 28-8. 59 knot) and a weight of 2. 17 and the lowest average wind speed (with the weight of 1. 33) is recorded in Hendijan, Omidieh, Behbahan, Lali and Dehdez. The province wind direction layer showed that western winds had the highest weight (2. 18) and southern winds had the lowest weight (0. 96). Conclusion: Understanding climate and its impact on transportation of troops, flight of fighters, movement of naval fleets, transport of heavy equipment, and performance of weapons in both sides of the war are among the most important determinants of victory or defeat in wars. Climate is one of the most important factor that directly affect military plans, and even national strategies, tactics, doctrines, command, organizational structure, optimal combination and type of navy-land forces, military and space equipment, collection of military information and clothes, maintenance, construction and support. In this study, eight factors including rain, temperature, dust, wind speed, wind direction, sunshine duration, humidity, and altitude were used in a combined approach. Geographic Information System and FTOPSIS model were applied in this approach. Results indicate that Khuzestan province is classified into five categories (from very unfavorable to very desirable) in regard to military activities. Most of the northeastern mountainous areas of the province are highly desirable, with excellent defensive capabilities. In Dehdez and Izeh, the proportion is somehow favorable. Eastern and southern parts of the province are very undesirable regarding the impact of climate parameters. The results also showed that wind speed and temperature are the most effective factors influencing the performance of military forces in the province.

Introduction: Currently, two general methods are used for classification of digital satellite images: pixel-based and object-oriented processing. Unlike pixel-based Methods, object-oriented techniques employ different geometric, spatial, spectral, and form-based algorithms, and selecting the most efficient algorithm in this process requires a lot of experience in image processing. In addition, multiple algorithms usually offer different results and this in many cases makes the selection of efficient algorithms difficult. In general, pixel-based classification includes supervised and unsupervised methods. Examples of these methods include maximum likelihood, neural network and support vector machine. Maximum likelihood method is one of the most effective methods used for image classification. Object-oriented methods take advantage of knowledge-based algorithms, and thus overcome problems pixel-based method faces because of not using geometric and textual information. In order to achieve high classification accuracy, two methods of pixel-based and object-oriented classification are compared in this research. On the one hand, integrated planning and management of urban areas, and on the other hand, collecting reliable information regarding land use makes this kinds of studies indispensable. Materials&Methods: Present study seeks to extract urban land use map. Thus, necessary data was received from Sentinel-2. Moreover, ENVI 5. 3, eCognation 9, SNAP, ArcGIS 10. 3, Google Earth, and land-use data were also used to process images and analyze data. In SNAP, atmospheric correction process was performed on images collected from the study area using SEN2COR plug-in. Samples collected from each class of Sentinel-2 satellite image were mapped on the image area. Pixel classification algorithms, support vector machines, maximum likelihood, artificial neural network, Minimum Distance to Mean (MDM), parallelepiped and Mahalanobis distance were used. Finally, land use classes (residential, gardens and green spaces, wastelands and passageways) in the study area were mapped using different classification algorithms. For object-oriented classification using nearest neighbor algorithm, the satellite image was first segmented in eCognation software using the Multiresolution Segmentation Algorithm. Parameters such as scale, shape and compactness were also studied in the image segmentation stage. Through trial and error, an appropriate value was selected for parameters used in segmentation. For practical comparison of the results, the same educational data was used in both object-oriented and pixel-based classification methods. Then, the most important methods for assessing accuracy including overall precision and kappa coefficient were extracted. Results & Discussion: As one of the most important methods used for extracting information from remotely sensed images, classification allows users to produce various types of information such as coverage maps, and land-use maps. Classification of satellite data includes segregation of similar spectral sets and classification of sets with the same spectral behavior. Regarding the resolution of images used (10 m) in this study, only 4 land-use classes possessed the required resolution capability for pixel-based classification of Sentinel-2 satellite images. These classes include built-up (residential) area, waste land, urban green space and street network. In this regard, support vector machine, maximum likelihood, artificial neural network, Minimum Distance to Mean, parallelepiped and Mahalanobis distance were used for classification. Classification results indicate that compared to other pixel-based methods, maximum likelihood method and Minimum Distance to Mean method show a precision of 85% or higher. In present study, geometric properties of land use classes (including scale, shape, and compactness) were used for segmentation and this process was performed by multiresolution method. For this purpose, results of image segmentation process were analyzed based on different parameters (with different scales) and spatial resolution of the image. In this way, appropriate values for segmentation were selected based on the specific features of the study area (an urban environment) through trial and error. Then, the proper image segmentation was selected and prepared for the classification stage using the above mentioned parameters. In the next step, 20 effective parameters including statistical indices, mean score of bands, NDVI index, standard deviation of the bands and geometric index were used for classification. Conclusion: The present study took advantage of six pixel-based methods (Support Vector Machine, Maximum Likelihood, Neural Network, Minimum Distance to Mean, Parallelepiped, and Mahalanobis) along with object-oriented classification method to produce a land-use map for Zanjan city. The accuracy of classification in different methods were compared and statistically analyzed using overall accuracy coefficient, kappa coefficient, user’ s accuracy, and producer’ s accuracy. The results of statistical analysis of the accuracy coefficients indicated that Minimum Distance to Mean and Maximum Likelihood method-with a Kappa coefficient of 90% and 85% respectively-are acceptable methods for land use mapping. Moreover, comparing pixel-based and object-oriented methods, it is possible to conclude that object-oriented approach with a Kappa coefficient of 0. 95% and overall accuracy of 97. 9% shows a higher potentiality. Nearest Neighbor algorithm is one of the most important reasons for achieving this high accuracy in object-oriented classification. In addition to the spectral information, this method uses information collected about issues like texture, form, position, and content for the classification process. Methods used in this study prove the accuracy of objective-oriented technique by employing effective parameters and developing rules to modify the initial classification of object-oriented technique. Another advantage of object-oriented method (as compared to pixel-based methods) is that apart from spectral information and statistical data, it is possible to apply several other indicators such as shape, texture, color, dimensions and altitude of the phenomena in the final land use map produced by this method. Finally, it should be noted that object-oriented classification has been developed for high resolution spatial data.

Introduction: Runoff is considered to be an effective variable in the formation and intensity of floods, and water balance. It is also considered to be a very important parameter in water resources management. Surface runoff is formed due to a combination of different parameters, such as severe precipitation, a sloping ground, and saturated soils. It is especially important to predict and determine the amount of runoff produced and transferred to the basin outlet. Generally, different parts of large basins may experience a higher or lower than average precipitation, and thus different spatial distribution of precipitation. Empirical formulas may sometimes give us an inaccurate estimation of the surface runoff volume. Radar and rain gauges are the most common tool used for collecting rainfall data. Together, they can be useful for estimation of rainfall volume and its distribution in a wide area. Integrating high resolution radar based rainfall estimation with hydrological models makes a highly efficient tool for flood prediction. Materials and Data: Baghu Basin is considered to be one of Gorgan Gulf sub basins. It covers an area of 24. 4 square kilometers. Its perimeter is 23. 2 kilometers. The length of its main river is 10 kilometers. The maximum altitude of the main river is 2080 m and its minimum altitude is 80 m. The river channel has an average slope of 20%. Data used in this research includes: 1-data received from east Caspian radar; 2-precipitation and daily evaporation data received from different weather stations around the basin, including Bandar Gaz, Bandar-Torkman, Hashem-Abad and Gorgan stations; 3-discharge value in previous floods of Baghu basin. Geographic coordinates of the basin were obtained using GIS. Geographical coordinates of the basin perimeter were also extracted by radar and the basin area was defined for the radar. Then using the radar software, total amount of precipitation and runoff were measured in the basin. These were used in (1) to calculate runoff coefficient, as a percentage of rainfall: (1) Where C is runoff coefficient, P is precipitation elevation and R is direct runoff. Discussion and Results: It is important to consider the effect of climatic and meteorological factors on runoff formation in basins. Severity of precipitation is the first factor. Radar based rainfall estimates indicated that increased rainfall intensity results in increased hourly runoff in the basin. The same phenomenon has been observed in some of previous floods in Baghu basin. In these cases, a sudden increase in precipitation resulted in a severe runoff increase. These floods exhibited long sharp-crested hydrographs. Spatial/temporal distribution of rainfall intensity was the second factor with a significant effect on the amount of runoff produced. Thus, the effect of rainfall distribution was also analyzed. Results indicate that rainfall distribution has affected the amount of runoff produced in different parts of the basin in different ways. Rainfall continuity was the third climatic factor with a significant role in the production of increased runoff. This factor was investigated in winter (cold seasons) floods. Apart from the intensity and volume of precipitation in these floods, precipitation continuity was the most influential factor in the production of a large amount of runoff. This shows the effect of rainfall continuity on runoff increase. Temporal distribution of rainfalls was the fourth factor influencing runoff production, and thus soil moisture. In winter, soil moisture is usually high and there is little evaporation. So soil maintains its moisture and remains wet for a longer time. In this way, a moderate and low amount of rainfall over a short period of time results in soil saturation, and runoff increase. This was investigated in Baghu basin precipitations. According to the findings of this study, increased soil moisture has resulted in runoff increase. Several climatic factors contribute to increased runoff coefficient. In high intensity floods which occur due to large volume of precipitation over a longer period of time, a huge amount of runoff would form. And if as a result of successive precipitation these factors combine with soil moisture, runoff coefficient would be even larger. In cold seasons, three factors-rainfall continuity, soil moisture and poor vegetation-results in increased runoff. However, dry soil and vegetation during warm seasons reduce the effect of intense precipitation on increasing runoff volume. Conclusion: Based on the findings of the present study, it is not possible to consider a single constant runoff coefficient for the total area of a basin. Thus, it is better to determine a range of runoff coefficients for each basin. It should also be noted that each flood has its own runoff coefficient, which depends on precipitation severity, temporal/spatial distribution, rainfall duration, intensity variations during precipitation, time intervals between each rainfall occurrence and season rainfall coefficient. Respective severity or weakness of different factors, combination of various factors affecting runoff, and the amount of runoff in similar precipitation may also vary. The present study indicated that due to severe and sudden rainfalls, warm season floods had long sharp-crested hydrographs. In winter, rainfalls were continuous, but with lower intensity. Thus, their hydrograph was wider than warm season floods. In small areas with less than an hour concentration time, the effect of spatial/temporal dispersion of rainfall on the amount of runoff is important. In Baghu basin, 8 to 25 percent variation was observed in runoff coefficient of eight different floods.

Introduction: Natural and anthropogenic hazards have always endangered human society and settlements. These hazards disrupt normal life of human society and lead to crisis. In this regard, it is necessary for all sectors and institutions in the country to adopt smart strategies for dealing with these natural crises. Moreover, there should be a continuous emphasis on political and economic challenges. As an effective, efficient and precautionary strategy, passive defense explains related requirements and mechanisms, and thus can lead to a reduction of damages resulting from different crises. Materials and methods: The present applied research takes advantage of a descriptive-analytic method. With the aim of reducing vulnerability of rural border hazardous areas in Marivan County, it investigates passive defense indices from a spatial and comprehensive perspective. In this way, vulnerable areas are identified and appropriate strategies are selected in accordance with the situation of passive defense in villages of this county. Required information was collected using documentary resources, especially basic geological maps and land use of the region. Then, indices were weighed using binary comparison method and fuzzy logic, and a final compiled map was prepared for the situation of passive defense. In this regard, 14 security, social and natural indices were selected as passive defense indices in accordance with the regional conditions. Afterwards, indices were weighed using AHP method and a Fuzzy weight was assigned to each layer. Accordingly, specified indices were mapped and compiled using Arc GIS. Results and Conclusions: The present study takes advantage of a comprehensive and spatial approach to investigate human and natural standards of passive defense in villages of Marivan region in Iranian border. Findings indicated that human standards, including population of villages, distance from main roads and village centers are the most effective and important parameters influencing the status of passive defense in the studied villages. Also, final zoning indicated that the region is mostly in an appropriate status regarding passive defense. But regarding the spatial zoning, most villages (89 villages) are classified as average (quite appropriate). Regarding passive defense, more than 65% of the villages in this county are classified in a quite appropriate (average) class, which indicates that short-term and applied programs must be rapidly adopted to improve the fragile condition of these villages and prevent from deterioration of their status in passive defense. Moreover, more than 75 percent of low populated villages (i. e. fewer than 150 people) are classified as average. Since services and facilities are also allocated based on the village population, it should be expected that low declining population of villages rapidly increases vulnerability and reduces the desirability of their passive defense status. On the other hand, among six rural district centers in the study area, five rural district centers are in an average condition and only one is in a good condition. In rural planning system of the country, rural district centers are considered to be the focal point of rural areas, and provide the most developed services. Thus, it is necessary to use these rural districts as communicational centers for crisis management. 84% of rural district centers in the study area are in an average or fragile state, which may increase vulnerability and result in serious concerns regarding the status of passive defense in these villages. Also, random and normal distribution of these villages is considered to be a relative advantage for passive defense situation. But based on this principle, similar actions must be carried out in all villages to improve passive defense status throughout the rural district. According to findings and field observations, following solutions are offered to improve the status of passive defense in villages of this county:-Considering principles of passive defense in planning of rural development in the county.-Moving toward human centered passive defense through people participation and their education.-Moving away from a purely military and structural viewpoint toward passive defense by officials responsible for city development and security.-Paying attention to standards of passive defense with the aim of determining the most appropriate rural centers or rural districts.-Detailed and accurate short term investigation of the status of passive defense in rural districts and elimination of deficiencies and weaknesses, especially regarding human standards, with the aim of improving the status of these centers.-Compliance with the principle of spatial justice in accessing and distributing services and economic and social facilities in villages of the county with an emphasis on low-populated and remote villages.