Nowadays, the maximum operation of GROUNDWATER resources has been achieved in Iran. Also, the majority of extractable water resources are utilized and the managing of water resources in the future is depended on more extracting of water resources. For better basin management, forecasting the GROUNDWATER DEPTH fluctuations in particular in arid areas is more necessary. In this study, time series spectral analysis is used to forecast the GROUNDWATER DEPTH fluctuations of Chamchamal plain. In this regard, the monthly GROUNDWATER DEPTH time series during 1995 to 2009 years are used for calibration periods and the periodogram diagrams are depicted. Data periodicity is analyzed by using Fourier spectral analysis and the deterministic term of data periodicity is eliminated. In the next step, stationary and normality in the data are considered. After that, the different time series models are fitted for the prepared data and accuracy of them were assessed by Akaike (AIC) criterion. The results show that ARMA (2, 1), ARMA (1, 1), ARMA (1, 1) models are the best fitted models for the measured data in Bazanabad, Gheshlaghabad and Gavkol piezometers, respectively. Finally, the residuals stationarity assumption test is used to check for the correct diagnosis of the fitted pattern. In this study, the results represent the high performance and accuracy of the applied new approach to the time series spectral analysis for forecasting GROUNDWATER DEPTH by application of the regression coefficient amount of 0. 78 and SI-Index of 4% to 14% of piezometers' data. Using spectral analysis, as has been provided in this study, is very useful for forecasting GROUNDWATER DEPTH.

Current study was conducted in order to finding the best models to estimating GROUNDWATER DEPTH using Hyperion hyperspectral satellite imagery in the sugarcane fields located in the southwest of Iran. For this purpose ground water level was measured in 132 observation wells from the beginning of May 2010 till end of September 2010, twice per week, in the Hakim Farabi farming and industrial lands. Moreover, from the other collected information like daily weather information, age and variety of sugarcane, planting and harvesting date of plants, managerial operations such as date and amount of the fertilization, irrigation and drainage information in the Hakim Farabi farming and industrial lands were used. In a same time with measuring the ground data, a hyperspectral satellite image of Hyperion sensor was acquired on September 2, 2010. After applying necessary pre-processing on the image, the changes in the spectral reflectance of the sugarcane under different values of GROUNDWATER DEPTHs was studied. Afterwards, it was tried to obtain appropriate models for estimating ground water DEPTH. For this purpose, capability of 21 vegetation indices, related to defferent regions of spectral reflectance of crops, was studied. Besides of these indices three new vegetation indices (SWSI-1, SWSI-2 and SWSI-3) were developed in this study. Results show that, variations of GROUNDWATER DEPTHs have a significant effect on spectral reflectance of sugarcane. Among the vegetation indices, indices related to water absorption bands or based on a combination of chlorophyll and water absorption bands had the highest correlation with GROUNDWATER DEPTH. Obtained models from the two vegetation indices developed in this study (SWSI-1, SWSI-3) and NDWI yield the best results for estimating GROUNDWATER DEPTH with R2 of 0.48, 0.48 and 0.47 and root mean square errors of 8.20, 8.25 and 7.98 cm respectively. Conclusions from this study indicate that using hyperspectral satellite imagery to monitoring water table in the sugarcane fields has an acceptable, fast and economical results.

Successive drought events as one of the most important environmental crises, along with population growth and uncontrolled water extraction, have led to an increase in the DEPTH of GROUNDWATER, especially in arid and semi-arid regions. The purpose of this study is to investigate the geostatistical relationship between GROUNDWATER DEPTH data and GROUNDWATER DEPTH based on precipitation data in three observation wells located in Fars province. These wells were selected based on the PSO clustering technique. Thus, the three observation wells that were closest to the center of the calculated clusters were selected as the representative of the clusters. These wells are located in Karsia, Dolatabad, and Fatehabad regions for clusters 1 to 3, respectively. Monthly GROUNDWATER DEPTH data has been used from 2003 to 2017. Kriging and cokriging methods were performed in the GS+ environment. In this research, precipitation data was used as an auxiliary variable. Furthermore, the models were selected based on the lowest RSS values and the nearest R2 values, and the spatial structure ratio (C / C0 + C) to one. Accordingly, the selected models for the main variable (GROUNDWATER DEPTH) in the first to third clusters are spherical, power, and linear, respectively, and for crossvariogram models (precipitation-GROUNDWATER DEPTH) are all spherical. The results showed that in the validation and test stage, the Cokriging method has higher accuracy than the Kriging method. The test stage in kriging and cokriging methods for RMSE index are (0. 92 and 0. 41), (0. 54 and 0. 52), and (1. 25 and 0. 95) in 1, 2, and 3 clusters, respectively.

Permanent exploitation of the GROUNDWATER resource necessitates the adoption of proper policies for the quantitative and qualitative protection of this valuable resource. Given the fact that parts of the Mazandaran province's development plans rely on the GROUNDWATER resources, the sustainability and trend of changes in DEPTH to GROUNDWATER (GWD) in this province were investigated in this study from 1991 to 2020 (1991 to 2000: first decade, 2001 to 2010: second decade, 2011-2020: third decade). Trend analysis was performed using the Mann-Kendall test and Sen's slope estimator. The sustainability of GROUNDWATER resources was assessed using four indicators: renewable GROUNDWATER resources per capita (GWRRp), total GROUNDWATER abstraction to recharge ratio (TAGR), total abstraction of GROUNDWATER to the exploitable GROUNDWATER resources (TAGE), and water exploitation index (WEI). The Man-Kendall statistic values ranged from -2.50 to 3.76, indicating that GWD has increased over the last 30 years in all locations except in Babol-Amol in the second and third decades and Noor-Noushahr in the third decade. The amount of GWD in Mazandaran province increased by 0.21 and 0.12 m per year during the first and second decades, respectively.  The west of Mazandaran had better conditions than the province's middle and eastern areas, according to GWRRP, TAGR, and TAGE indices. According to the WEI indicator Ramsar-Chalous and Noor-Noushahr areas (WEI<10), Behshahr-Bandargaz, Babol-Amol, and Sari-Neka areas (1020) were in satisfactory, worrying, and critical state, respectively. Results showed that the GROUNDWATER resource situation in a vast section of Mazandaran is unsustainable and proper management measures are needed to avoid the situation from worsening.

A concern that researchers usually face in different applications of Artificial Neural Network (ANN) is determination of the size of effective domain in time series. In this paper, fractal analysis was used on GROUNDWATER DEPTH time series to determine the size of effective domain in the series in an observation well in Union County, New Jersey, U.S.The variation method was applied to the sets considering different domains of 20, 40, 60, 80, 100, and 120 preceding days and the fractal dimension was determined.The fractal dimension remained constant (1.52) when the length of the domain decreased below 80 days. Data sets in different domains were fed to a Feed Forward Back Propagation ANN with one hidden layer and the the GROUNDWATER DEPTHs were forecasted. Root Mean Square Error (RMSE) and the correlation factor (R2) of estimated and observed GROUNDWATER DEPTHs for all domains were determined. In general, GROUNDWATER DEPTH forecast improved, as evidenced by lower RMSEs and higher R2s, when the domain length increased from 20 to 120. However, 80 days was selected as the effective domain because the improvement was less than 1% beyond that. Forecasted GROUNDWATER DEPTHs utilizing measured daily data (set #1) and data averaged over the effective domain (set #2) were compared. It was postulated that the more accurate nature of the measured daily data was the reason for a better forecast with lower RMSE (0.1027 m compared to 0.255 m ) in set #1. However, a major drawback was the size of the input data in this set which was 80 times the size of the input data in set #2; a factor that may increase the computational effort unpredictably. Hence, it was concluded that fractal analysis may be successfully utilized to lower the size of input data sets considerably, while maintaining the effective information in the data set.

GROUNDWATER resources are very important for agriculture. In this study the spatial variability of GROUNDWATER salinity for two years (2002 and 2007) and GROUNDWATER DEPTH for four years (1987, 1997, 2002 and 2007) was investigated using geostatistical methods in Mazandaran province. The interpolation methods were ordinary kriging and cokriging, and inverse distance weighing with distance power of 1 to 4. The performance of the prediction methods was evaluated though cross-validation with comparison criteria of root mean square error (RMSE) and mean bias error (MBE). The statistical analysis showed that GROUNDWATER salinity and especially GROUNDWATER DEPTH have a large variance and coefficient of variation. The average GROUNDWATER DEPTH increased from 1987 to 2007 and the average GROUNDWATER salinity decreased from 2002 to 2007. The geostatistical analyses showed that the GROUNDWATER salinity and DEPTH are highly spatially correlated. The spatial structure of GROUNDWATER salinity and DEPTH follow an exponential and spherical model, respectively. The results of crossvalidation indicated that the best interpolator for salinity estimation is inverse distance weighing with distance power of 1. For estimating GROUNDWATER DEPTH, inverse distance weighing with distance power 3 for 1987, ordinary kriging and cokriging with similar accuracy for 1997 and cokriging for 2002 and 2007 achieved the best results. The prediction map of GROUNDWATER salinity showed that the salinity was increased from west to east. Based on the map of GROUNDWATER DEPTH, south and southeast regions have the highest DEPTH of GROUNDWATER. In addition to estimation map, kriging methods were able to present the map of estimation variance for GROUNDWATER DEPTH. These maps showed that the estimation uncertainty is smaller at sampling location and for nearby samples, and is larger as samples are more distant or where there is no samples. Therefore, when it is necessary to present the estimation uncertainty rather than estimation only, kriging should be preferred to inverse distance weighing.

Since the increase in the DEPTH of GROUNDWATER and its intensification can indicate serious limitations in the exploitation of these resources, predicting the changes of this parameter plays an important role in managing these resources and preventing possible damage to them. For this purpose, the use of smart methods has been strongly recommended by researchers. In this research, the methods of multilayer perceptron neural network (MLP), fuzzy inference system (fis), adaptive neural fuzzy inference system (ANFIS), and the combined method of fuzzy neural inference system and particle swarm optimization (ANFIS-PSO) were used for simulation of GROUNDWATER Fluctuations DEPTH in Haji Abad area between March 1995 to October 2022 on a monthly scale. The training and testing phases were done with 75 and 25 percent of data, respectively. To measure the accuracy of the models, root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute value of error (MAE) indices were used. The best results in the training phase are related to ANFIS-PSO, ANFIS, and MLP models, respectively. Simultaneously with the training of the mentioned models, the testing stage of said models was also implemented. Finally, the best results in this stage belonged to the neural network model with time delay [1 3 5], the ANFIS-PSO model with time delay [1 2 3], and the neural network model with time delay [1 2], respectively. The accuracy indices in the test stage for the best models are (0. 1871, 0. 1865, 0. 1857) for RMSE, (0. 7402, 0. 6715, 0. 6684) for MAPE, and (0. 1326, 0. 1238, 0. 1198) for MAE, respectively. These values show that all three models have an error of less than 20 cm, an error percentage of less than 0. 75%, and an absolute error of less than 14 cm, which indicates the acceptable accuracy of these models. Also, the coefficient of determination obtained from the regression relationship of the calculated and measured values of the GROUNDWATER DEPTH in the test phase for all three models is around 0. 82, which indicates a relatively high linear relationship between these two parameters.

One of the important research areas in protection of underground structures is evaluation of projectiles penetration DEPTH in ground as a protective cover. The phenomenon of impact and projectile penetration is dependent on several factors, one of the most important of them is the effect of soil moisture. Therefore, this paper is aimed to study the GBU-28 penetration DEPTH in sandy and clayey soils and the effect of GROUNDWATER level on penetration DEPTH by means of AUTODYN software. The results have shown that soil moisture plays an important role in projectile penetration DEPTH in different times of explosion. Generally, projectile penetration DEPTH in clayey soil is more than sandy soil with same densities, but increasing rate of projectile penetration DEPTH compared to dry soil in sandy soil is a little more than clayey soil.

Introduction: GROUNDWATER is the most important resource of providing sanitary water for potable and household consumption. So continuous monitoring of GROUNDWATER level will play an important role in water resource management. But because of the large amount of information, evaluation of water table is a costly and time consuming process. Therefore, in many studies, the data and information aren’t suitable and useful and so, must be neglected. The PCA technique is an optimized mathematical method that reserve data with the highest share in affirming variance with recognizing less important data and limits the original variables into to a few components. In this technique, variation factors called principle components are identified with considering data structures. Thus, variables those have the highest correlation coefficient with principal components are extracted as a result of identifying the components that create the greatest variance. …