In this research, a hybrid wavelet-artificial neural network (WANN) and a geostatistical method were proposed for spatiotemporal prediction of the groundwater level (GWL) for one month ahead. For this purpose, monthly observed time series of GWL were collected from September 2005 to April 2014 in 10 piezometers around Mashhad City in the Northeast of Iran. In temporal forecasting, an artificial neural network (ANN) and a WANN were trained for each piezometer. Kriging was used in spatial estimations. The comparison of the prediction accuracy of these two models illustrated that the WANN was more efficacious in prediction of GWL for one month ahead. Thereafter, in order to predict GWL in desired points in the study area, the kriging method was used and a Gaussian model was selected as the best variogram model. Ultimately, the WANN with coefficient of determination and root mean square error and mean absolute error, 0.836 and 0.335 and 0.273 respectively, in temporal forecasting and Gaussian model with root mean square, 0.253 as the best fitted model on Kriging method for spatial estimating were suitable choices for spatiotemporal GWL forecasting. The obtained map of groundwater level showed that the groundwater level was higher in the areas of plain located in mountainside areas. This fact can show that outcomes are respectively correct.

Considering the climatic condition, drought is an inevitable phenomenon in Iran. The probability of the drought occurrence can however be predicted using the recorded meteorological data. Due to the drought importance and its impact on groundwater resources, the influence of drought on groundwater quality and ground water table of Qorveh-Chardoli plain was evaluated in this study during the last 25 years period. Monthly precipitation data (1987-2013) were applied to calculate the standardized Precipitation Index (SPI) which was then used to find the dry and wet years. Groundwater quality was also determined using electrical conductivity and sodium adsorption ratio and considering the Wilcox diagram. Kriging map of the water quality and water table were produced for wet and dry years. Groundwater quality of the Qurveh-Chardoli aquifer were classified as C2S1 and C3S1. There was no significant change in the water quality even during the wet period with raising water table. Groundwater level has changed 29.35 meters (increasing) and 13.39 meters (decreasing) during the dry and wet periods, respectively. The overall rate of decrease in the water level during the study period was 49 centimeter per year. The greatest decrease in water level were observed for eastern and southern parts of the plain.

Numerical simulation of groundwater table variations can be an effective tool for water resources management. In this study, water table variations of Shahdad aquifer located in Kerman provience, were simulated using Groundwater Modeling System (GMS) software and MODFLOW model. Changes in the aquifer water level indicate a decrease in the aquifer in the northern, eastern, southern, and some central parts of the aquifer. The developed model was calibrated using the monthly water level measurements for the period of 2012-2013 and then validated using the monthly statistics for the water year 2015-2016. Aquifer properties including hydraulic conductivity, storage coefficient, and recharge rate were estimated by inverse modeling with PEST Code. The coefficient of determination for the validation stage was calculated as 0. 95, demonstrating the good consistency of observed and predicted data and the ability of the model to predict future aquifer water table variations. A sensitivity analysis showed that the model is more sensitive to the well discharge than to surface recharge or hydraulic conductivity. Monthly reduction of discharge from wells by 30 percent can stop this phenamena.

Aims: Management of water resources, especially groundwater in arid and semiarid regions, is of particular importance. Various natural and human factors in recent decades have created critical conditions for these resources. Therefore, this study was conducted to investigate changes in groundwater levels over the past 28 years. Materials and Methods: To conduct this research, statistics related to 64 piezometer wells were studied during the years 1990–, 2018, and the effect of rainfall and extraction from groundwater resources was interpreted and analyzed. Results: The results of the study showed that according to the hydrograph of 64 piezometer wells in Kashan plain, the groundwater level has a downward trend and has decreased by 15. 29 m during 28 years. The annual drop was 0. 54 m. An increasing peak of water table was also observed. Moreover, the water level has decreased slightly in some years and has not decreased in some years. Conclusion: The study shows that uncontrolled harvesting in the long run has had a more significant impact than rainfall on groundwater resources. Moreover, water abstraction has occurred on average in the southern and southeastern parts of the plain, which can be due to the concentration of agricultural lands in this area. To reduce this trend, strong management strategies should be adopted and appropriate to the situation.

Introduction Preservation and proper management of water resources are one of the essential fields of study in the world. In arid and semi-arid regions like Iran, quantitative and qualitative management of underground water resources is particularly important. In most hydrological issues and groundwater resources studies, groundwater statistics and information availability are critical. To collect information without side effects, comprehensive and sufficient data collection with the help of a groundwater monitoring network is very important. In line with the sustainable management of renewable water resources, the need for a network of underground water observation (monitoring) wells to accurately measure the water level is necessary and necessary. Considering the complexities of the underground water environment and the high costs of conventional monitoring methods, inventing new technologies and using advanced methods in this matter will significantly help improve the underground water systems. One of the parameters of particular importance in monitoring groundwater quantity is the groundwater level. Therefore, this parameter should be measured or estimated as accurately as possible. In recent decades, the use of computer and calculation models to monitor the level of underground water has developed significantly. Considering the importance of underground water resources and network monitoring, to save time and money, in this research, principal component analysis and Shannon's entropy theory were used to monitor the underground water network of the Dezful-Andimeshk Plain. Materials and Methods This research used monthly groundwater level information from 77 observation wells in the Dezful-Andimeshk Plain during 2018-2019. Groundwater level information is collected twice a month. Principal component analysis and Shannon entropy methods were used for monitoring. In the current research, the number of statistical periods for each well is 24, less than the total number of observation wells. Twenty-four observation wells around it were used to monitor each well. In groundwater level monitoring, the relative importance of each well is defined by the ratio of the number of times that well is recognized as a compelling well to the number of times that well is included in the analysis of the main components. This ratio shows the importance of each well compared to other wells. Therefore, to save time and costs, less important wells can be removed in the monitoring of the underground water level. In 1948, Shannon showed that events with a high probability of occurrence show less information, and on the contrary, the lower the probability of an event, the more information it provides.  In this method, the weight of each well was obtained using Shannon's entropy theory. Any well that has a higher Shannon entropy weight contains more important and unpredictable information and should be preserved. On the contrary, a well that has a lower Shannon entropy weight can be removed from the network. Principal component analysis and Shannon's entropy method in the current research were done with the help of coding in Matlab software due to the high volume of calculations. Results and Discussion To rank the wells, the threshold limits are equal to zero, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and one considered. At threshold one, only wells that have a rank of one remain (wells that are recognized as effective wells in all analyses) and threshold zero includes all wells (effective and ineffective). According to the obtained results, increasing the error in the threshold zero to 0.7 is gradual, but in the thresholds 0.8, 0.9, and one, the error value increases with a high slope. So, the amount of error in the thresholds of 0.7, 0.8, 0.9, and 1 has been calculated as 12.2, 17.7, 25.3 and 34.2 respectively. Therefore, the threshold limit in the current research is considered to be 0.7. However, the number of wells effective in monitoring the underground water level is reduced from 77 to 32. Shannon's entropy weight values were also calculated for all wells. 11 wells have the highest value of Shannon's entropy weight, which shows that they contain the most information. Conclusion The general comparison of the results of the two methods showed that all 11 wells with the highest weight in the Shannon entropy method were also observed as effective wells in the principal component analysis method. By knowing the effective wells in the region, firstly, in the face of lack of time and money, it is possible to use known effective wells for monitoring secondly, by removing ineffective wells, there will be little change in the average level of underground water. It is not possible, or in other words, the tracking error does not increase significantly. Comparing the results of the two methods showed that the remaining wells in Shannon's entropy theory are among the wells identified in the principal component analysis method. Also, considering that the wells in the region were built by the Khuzestan Water and Electricity Organization considering the types of uses, removing the ineffective wells will not affect the process of using the information of the wells. It is recommended to use principal component analysis and Shannon entropy for groundwater quality monitoring in the study area. Additionally, it is suggested to monitor the quality of the underground water network in the study area using the methods used in future research.

Understanding the trends of groundwater level changes and its prediction can be important in water resources management. In this study, the simulation of groundwater level changes in Nahavand plain was performed using MODFLOW mathematical model and considering the parameters affecting the phenomenon. First, the calibration and validation process was performed over a statistical period of 120 months. Then, four scenarios (current harvest conditions and 10, 30 and 50% reduction in agricultural wells harvesting) were investigated to predict future groundwater level changes. The results indicate that the mathematical model used to simulate the Nahavand aquifer has 21% relative error of NRMSE, which confirms the proper modeling after considering the prediction process. It was also found that 0 and 10% scenarios would decrease groundwater level in the future. A 30% reduction in operating capacity leads to constant conditions and a reduction of more than 50% creates a positive balance in the long term.

Groundwater resources are considered as one of the significant and economical water resources. Comprehensive recognition and proper utilization of this valuable resource especially in arid and semiarid areas has an important effect on sustainable development of social and economic activities. It is necessary to predict groundwater level fluctuations for a better understanding of the aquifer behavior in these areas. This research was aimed at prediction of groundwater level in Neyshabour plain using "Panel Data" model. The plain was selected due to presence of over 50 observation wells, mostly with more than 12 years of record. Inasmuch as "Panel Data" model considers historic data for several observation wells, it was able to predict groundwater levels in different observation wells simultaneously. At the first step, the available observation wells in the plain were clustered with "Ward" method which ended-up in six areal zones. Then, for each cluster, an observation well was selected as its representative, and for each zone, values of independent variables (precipitation, temperature, and initial water table) were estimated by "Distance Inverse method". Finally, the performances of different Panel Data regression models such as Panel Data with Common effects, Fixed effects and Random effects, for groundwater level prediction were investigated. The results shows Common effects model has the best results for prediction of groundwater level. The performance indicators (R2: 0.99 and RMSE=0.05) reveals the effectiveness of this method. In addition, these results were compared with the results of an ANN model, which showed relevant superiority of "Panel Data" over ANN.

Groundwater is the most reliable source of supply for potable water and supports a wide array of economic and environmental services. There is a significant concern that groundwater levels are declining due to intense aquifer use. The sustainable management of groundwater resources requires good planning and concerted efforts. To manage groundwater resources, it is necessary to predict the groundwater levels and its fluctuations. The prediction groundwater level can guide water managers and engineers effectively. On the other hand, there are multifarious types of equipment for measuring levels of groundwater. Sophisticated water level loggers or divers can measure the groundwater level automatically. Sounding devices with acoustic and light signals are also used to check groundwater levels. The use of devices for measuring the level of groundwater is timeconsuming and costly. To reduce the time and cost of the groundwater level measuring process, many methods of Artificial Intelligence (AI) have been utilized for estimating the groundwater level. Among the AI methods, SVMs has great ability in predicting non-linear hydrological processes. Support vector machines (SVMs) is as an intelligent computational method for predicting hydrological processes. Recently, (SVMs) have been successfully applied in classification problems, regression and predicting,as techniques of machine learning, statistics and mathematical analysis. The SVM is based on the structural risk minimization (SRM), which can escape from various difficulties, such as the necessity of a large number of control parameters and a local minimum in artificial neural networks (ANNs). The weighted least squares support vector machines (WLSSVM) was first introduced by Suykens et al., and has proved to be much more robust in several fields, especially for noise mixed data, than least squares version of SVM (LSSVM). Their powerful scientific research provides motivation for employing WLSSVM method in estimating groundwater level. The accurate value of WLSSVM parameters (γ, , σ, ) effect on the estimation, these optimal parameters can be achieved optimization algorithms. Therefore, weighted least square support vector machine (WLS-SVM) model was coupled with particle swarm optimization (PSO) and gravitational search algorithm (GSA) as metaheuristic algorithms for estimating well water level. In this study, an attempt has been made to use the hybrid model with high accuracy to estimate the groundwater level. In order to estimate the groundwater level, ten wells data in Bagheyn plain of Kerman province is considered during ten-year time series. The estimated value obtained by the WLSSVM-PSO and WLSSVM-GSA models are compared with the observed value, and showed the estimated results have nearly coincidence with observed values. Numerical results show the merits of the suggested technique for groundwater level simulation. In order to verify the hybrid learning machine metaheuristic model, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Average Absolute Error (AAE), and Model Efficiency (EF) are computed, and these statistical indicators stand on the good acceptable range, and find WLSSVM-GSA is more accurate than WLSSVM-PSO. The results demonstrate that the new hybrid WLSSVM-GSA model has high efficiency and accuracy with observed values, and the modelling method is an innovative and powerful idea in estimating well water level.

Jazmurian basin in the southeast of Iran is one of the most important and vital basins. Due to the lack of surface water resources and placing in the priority of use this basin is faced with a sharp decline in the level of underground aquifers. The aim of this study was to identify the factors affecting groundwater resources and predict the groundwater level and its variation in Jazmourian Basin. In this study by considering the importance of identifying the factors affecting the condition of groundwater resources and the causes of decline, initially, the geological and geomorphological features of the basin and its impacts on the quantity and distribution of the groundwater resources were studied. Then, the effect of hydrological droughts with using two streamflow index (SDI) and the standardized precipitation index (SPI) at hydrometric, pluviometric, and synoptic stations located in the basin aquifers was studied. The portion of perceptions on changes in groundwater resources by wells, spring, and aqueducts, as well as the impact of surface structures and expenditures on land surface level changes were determined. In addition, the average monthly and annual mean of groundwater levels during the years 1370-93 were investigated using time series models to predict groundwater changes by the year 1420. The study results on the effect of different factors on groundwater water changes showed that hydroclimatic droughts, although affecting underground water changes, did not have a significant effect. The dams and deep and semi-deep wells with negative correlations of 0. 83, 0. 75 and 0. 68 had the most negative effects on groundwater drops, respectively, and the average discharge of wells and springs increased significantly with increasing groundwater level. Also, the study of changes in groundwater level in the basin indicates a significant decrease (0. 37 m / year) and predictions show that in the coming years it will face more severe losses. The high level of decline was observed in the summer with a change of 1. 96% and in the autumn it was 1. 78% lower than the other seasons. Overall, the results showed that if the current trend of exploitation of the groundwater resources continues, the region will be facing more challenges.