In this study, for determination of environmental coastal sensitive areas in Sistan and Baluchestan province (Iran), the initial list of ecological and human criteria were identified in coastal and shore areas. Based on the identified criteria, a questionnaire was prepared and its qualitative and quantitative validities were determined. The final list of criteria and their weight achieved using Delphi and AHP method respectively. After preparing the criteria map (in ArcGIS software), both ecological and human weights aggregate individually during the overlaying process; and five sensitivity classes achieved. By using a decision-making matrix and the classification of ecological and human sensitivity map, final status of coastal environmental was determined. Results of the ecological zoning showed that 24.8% of land and 1.9 % of shore area were included in very high sensitivity class; and the widest range of human sensitivity (in both coastal and shore areas) was placed in very low sensitivity class. Based on the results of status analysis matrix, the weak and intensive classes of environmental sensitivity have the largest area, respectively. Considering that in this study area, care measures and development considerations have not been studied separately in three ecological, human and environmental sensitivities and this research is the first study, which has addressed this issue; the measures and considerations related to these areas, proposed for use in the Coastal Management Environmental Management Program (CEMO).

Introduction: In drainage and irrigation network capacity design and determination, reference evapotranspiration (ETo) plays significant role. Methods applied for estimated reference evapotranspiration classified in two direct and computational methods. Amongst computational methods it might point to Penman- Monteith method. This method requires radiation, temperature, humidity and wind speed data with high reliability rate in vast ranges of climates and areas represent precise outcome from reference plant Evapotranspiration. …

Floods, as natural and unexpected events, have occurred frequently in recent decades. To reduce the damages caused by floods and flood management, it is mandatory to assess the possibility of danger and prepare maps of possible danger zones. In recent decades, many destructive floods have occurred in the Nakarod catchment. Because of this, to manage floods, reduce damages, and properly use water resources, the flood potential of the sub-basins of the Nakarod catchment has been studied. Neka River is 176 km long, and it is one of the important rivers of Mazandaran province and one of the catchments of the Caspian Sea. In this research, to prepare a map of the sensitivity of the sub-basins to the flood risk of the basin, 11 influencing parameters have been used, which includes elevation, slope, distance from drainage network, drainage density, flow accumulation, rainfall, land-use, geology, stream power index, topographic wetness index and curvature of the topography. The layers were weighted using the Analytical Hierarchy Process (AHP); and eventually, by using the weighted linear combination method in ArcGIS software, the standardized layers were multiplied by the corresponding weight, and then the results of all the variables were added and accumulated together, and the final sensitivity map was divided into five classes. The results of the research indicate that flood sensitivity is different in the sub-basins of the Neka RiverAmong the effective environmental factors in flooding, the elevation and stream density were the most influential factors in the flood risk of the Neka catchment.

ecological zoning of the coastal areas of Rudsar based on criteria developed by Iran’ s Environmental Protection Organization with the purpose of identifying sensitive coastal marine zones. In this research, the coastal zone was divided into separate habitats based on morphology, and the zoning map of the sensitive coastal areas of Rudsar was drawn by studying suitability criteria in the habitats and through extracting the evaluation table. Based on the obtained results, the coastal zone was divided into 18 habitats. Sandy coasts with 8 habitats occupied most it (79. 49 percent of the coastal zone). Habitat number 17 with the score of 84 had the capacity to be considered a protected coastal marine zone, or a coastal marine wildlife refuge. 14 habitats had scores of higher than 50, and were considered sensitive coastal marine zones and 3 habitats with scores lower than 50 as free zones. According to the results most of the coastal area in Rudsar had medium sensitivity, and could be referred to as sensitive coastal marine zones.

Azarshahrchai catchment area, located on the western slope of the Sahand mountain range, is formed by the connection of numerous tributaries flowing in deep valleys, and every year in the spring season, with the onset of rains, floods occur in these valleys. The purpose of this research is to evaluate the performance of the weighted evidence (woe) function for preparing the flood susceptibility map of the Azarshahr Chai catchment area. In order to reach the mentioned goal, first, the location of 82 flood catch points using Landsat8.c2.l2 satellite images based on the flood of April 2016 was randomly divided into two groups of 70% (57 flood catch points) for training data and 30 percent (25 points) flood) was used for validation data. Then 14 effective factors in flood occurrence elevation, slope, Aspect, slope curvature, distance to river, distance to road, river density, TWI (topographic moisture index), lithology, soil type, rainfall, and NDVI in Arc software environment and land use in ENVI5.3 software environment, analysis, and location of flood catch points in all 14 factors were investigated. The ROC operating characteristic curve was used to validate and correct the results obtained. The results of the research indicate that 19.56% of the basin is in the very high sensitivity class, 19.18% in the high sensitivity class, 24.61% in the moderate class, 21.94% in the low class and 14.68% in the shallow class in terms of flood vulnerability.

Zoning of gully erosion susceptibility and determining the factors controlling gully erosion is very important and vital. The aim of this study was to investigate the spatial distribution of gully erosion using two models of ANN and MaxEnt and to determine the factors affecting this type of erosion in Golestan Dam basin. Therefore, 14 factors in the form of three divisions, including topographic factors, other factors and combination of factors )14 factors) were considered as predictors of sensitivity. Out of 1042 gully erosion points, 30 and 70 percent were randomly classified as validation and test data, respectively. The results of Jackknife test showed that the parameters of height, rainfall and depth of valley are the most important variables affecting the prediction of gully erosion. The results of the modeling showed that the best accuracy of the model based on the ROC curve in the training model )0. 923) and in the validation, stage )0. 902) was the artificial neural network model, and this condition is achieved when all the factors in the modeling be involved. According to this model, about more than 20 percent of the domain )45633 ha) has a high sensitivity and is very susceptible to gully erosion.

Facilities built in areas affected by earthquake activity, such as tunnels, which have always been an integral part of human life, must withstand both dynamic and static loading. It has led to the need for practical studies on the effects of earthquakes on underground structures and the factors affecting their destruction. For this purpose, in this research, at first different patterns of tunnel’s excavation were investigated and by using Plaxis 2D software and based on Tabas earthquake in Iran, sensitivity analysis on geotechnical parameters of the soil surrounding tunnel such as cohesion, friction angle, unit weight and modulus of elasticity was carried out, and the parameters whose changes have the greatest and least effects on the bending moment changes on the tunnel lining are introduced. The results show that tunnel excavation patterns significantly affect the bending moment, axial forces, displacements, and surface settlement of the tunnel. Often, by dividing tunnel excavation area to small parts, the values of bending moment, axial forces, displacements, and surface settlement of the tunnel decreases in static analysis. Also, outputs of sensitivity analysis on geotechnical parameters of the soil surrounding tunnel showed that modulus of elasticity of the soil surrounding tunnel has the most effect and cohesion changes have the least effect on bending moment induced on tunnel lining..

The purpose of this study was to evaluate the performance of two data mining models; artificial neural network and vector support machine algorithm in three modes: Using morphometric indices including topographic wetness index, topographic position index, stream power index, length slope index, terrain ruggedness index, mass balance index, profile curvature index and surface curvature index ; Using environmental and human factors including rainfall, basin height, slope, slope direction, lithology, land use, normalized vegetation difference index, distance from stream, distance from road, and distance from fault; And a combination of the above two conditions in zoning the landslide sensitivity of the Cherikabad watershed in Urmia. For this purpose, 92 landslide points in the watershed were identified using field study and Google Earth images. The map of morphometric indices and maps of environmental and human factors were prepared and digitized in ArcGIS10.5. The evaluation results of the two models using the ROC curve showed that in the case of using only morphometric indices, the two models SVM and ANN with the area under the curve of 0.742 and 0.763, respectively, have good performance in landslide sensitivity zoning. In the case of using human and environmental factors, the above two models with an area under the curve of 0.876 and 0.929 have good and very good performance, respectively; and in the case of using both human and environmental factors along with morphometric indices, the two models with an area under the curve of 0.940 and 0.936 had almost the same performance with excellent rank in the zoning of sensitive areas. Moreover, the highest quality­ sum (Qs) and Density ratio (Dr) had the highest correlation between risk categories for the SVM model in the third case. The results of Kappa index in the superior state showed that lithology, LS, and basin height factors had the greatest effect on the occurrence of landslides, respectively. Therefore, the effects of natural factors in comparison with human factors, and in general, the morphometric indices are higher in the occurrence of landslides than environmental and human factors, and the basin is inherently sensitive to landslides.