IntroductionWater is the source of life and a strategic resource for human societies. The need for this vital resource is increasing exponentially due to the increase in population and the development of industry and agriculture. People are forced to use underground water because surface water is not generally and permanently responsive to diverse needs. A decrease in their volume and many problems have been caused by the excessive use of these resources. This crisis has caused regional crises caused by the imbalance of resources and consumption, along with climate changes, has raised the issue of integrated management of water resources more than ever. Agricultural land has been developed due to the increase in population and the need for more food. Programs without principles that rely solely on the quality and quantity of underground water resources have been harmful. Groundwater aquifers are transformed into sources of the country's needs due to the heterogeneous and untimely temporal and spatial distribution of discharges and surface water flows. In recent years, with the increase in water demand and the non-supply of a significant part of it by surface water sources, the extraction - permitted and unauthorized - of underground water sources has been given much attention; so that the level of underground aquifers has decreased dramatically across the country. The purpose of the present study was to investigate the impact of the important variables of precipitation, inflation and annual population as a representative of climatic, economic and social factors on the fluctuations of the underground water level in Urmia region. Material and MethodsIn the present study, the impact of three factors, precipitation, population and inflation, on the subsidence of the Urmia Plain aquifer has been investigated. To do this, multiple linear regressions was performed between the data of the annual loss of the groundwater level during 38 years, 1981 to 2019 with three variables of precipitation, population and inflation index of the previous year. According to the previous researches, firstly, an index of inflation has been established by comparing the average loss of the piezometric level of the underground water in  Urmia region as a dependent variable, with the three independent variables of the average rainfall of water year as the most important climatic factor, the annual population of the major centers of human concentration located in the Urmia plain of previous year, and the base coefficient of the annual monetary value of previous year compared to 1981 using a multivariable linear regression. Then, the outcome is compared to the outcomes of artificial neural networks such as four-layer perceptron, three-layer perceptron, and radial basis function. All three networks have an input layer with three neurons to receive the values of the three independent variables of precipitation, population and inflation. One or two hidden layers with a number of neurons, to perform calculations and process the relationship between independent and dependent variables; and an output layer with a neuron to provide the processing results i.e., the estimated aquifer subsidence rate. The data used in the present study were derived from the years 1981-2019. The reference of the aquifer level data is the hydrograph extracted from 67 piezometer wells in the area by the underground water unit of basic studies of the West Azerbaijan Regional Water Company. The annual rainfall data reference is of the Urmia camp evaporation station located in the company premises, which is well controlled and highly reliable as an indicator of rainfall changes in the region. Population data is sourced from the Iranian Statistics Center, while inflation data is sourced from the Central Bank of Iran. Results and DiscussionAccording to the results of the reviewed models, despite the differences in the values of the numerical results, in all four models: multivariate linear regression, perceptron artificial neural networks of the four layers MLP:3-2-2-1, and the three layers MLP:3-5-1 and the radial basis function RBF: 3-5-1, it can be seen that the importance of the independent variables under study are population, inflation and annual precipitation respectively. It is obvious that a larger population needs more food, clothing, housing, etc., which, according to the concept of virtual water, ultimately leads to more use of the limited available water and soil resources. Economic activity, particularly agriculture, is increased due to the depreciation of currency and decrease in people's purchasing power, which is a result of the decrease in purchasing power and the depreciation of currency. This problem has also led to the change of land use of natural resources to agricultural lands that are either rainfed or irrigated. Explaining that rain fed lands cause more rainwater loss through capture and then evaporation and transpiration by plants planted by farmers. Irrigation of agricultural plants or gardens of irrigated lands - mainly with unauthorized water harvesting - ultimately leads to more water consumption. Additionally, humans have exploited underground water resources due to the inappropriate and untimely distribution of rainfall and surface water resources. Although by adopting new management methods, both social and economic, and improving water productivity, despite the increase in demand for water, despite our efforts to protect this vital, sensitive, and strategic resource, statistical studies, including the current results, demonstrate that we have not chosen the correct solutions. Considering some irreparable effects of the aquifer level drop, including irreversible changes in the mechanical characteristics of the soil, which lead to more vulnerability of infrastructures and facilities; the emphasis is placed on comprehensive water resource management and the concept of virtual water and its trade.

Introduction Salmas Plain represents one of the most critical areas in the country experiencing subsidence. In general, various factors cause land subsidence, but in many areas, the excessive extraction of ground water from aquifers causes land subsidence. The increasing use of ground water, especially in the sites that are accumulated with alluvial deposits, shallow sea or unconsolidated lake, leads to subsidence or collapse of the land. With the excessive extraction of ground water, the water level of the aquifer decreases and the hydrostatic pressure decreases, which makes it possible for the land to subside gradually. Subsidence in plains mostly occurs due to this factor, namely excessive groundwater extraction and compaction of clay and silt layers between aquifers. In this case, even if the water table level rises again, the land cannot return to its original level.   Materials and methods In this study, the susceptibility of land subsidence in Salmas Plain was investigated using layers of influential factors in subsidence with ArcGIS software and fuzzy logic. In the first stage, statistical information on some factors causing subsidence, including groundwater level decline, well extraction rate, aquifer storage coefficient, transmissivity coefficient, precipitation, DEM map, soil texture, and bedrock depth, was collected and raster maps of each of these factors at the aquifer level were prepared. In the next stage, fuzzy layering was performed using fuzzy membership functions based on the impact of decreasing or increasing each of these factors on land subsidence. Subsequently, the maps were combined using fuzzy operators (Gamma OR, AND, SUM, PRODUCT) to obtain a unified map of aquifer subsidence susceptibility. Finally, to select the best combination of operators, the results were compared and evaluated with field observation data and the ROC curve performance index.   Results and discussion The results showed that the OR operator had the lowest conformity with observed subsidence in the area with an AUC of 0.693. Gamma operators with an AUC above 70% had the highest overlap or conformity with observed subsidence in the plain. In this study, the Gamma 0.9 operator was selected as the best fuzzy operator with an AUC of 0.805. The results indicate that the eastern part of the aquifer is critical in terms of subsidence. Approximately 25% of the total area of Salmas Plain, equivalent to 93 square kilometers, has subsidence with very high susceptibility.   Conclusion Based on the results obtained, it can be said that although the AUC value of the fuzzy operator sum is higher, the Gamma operator with a value of 0.9 has the highest conformity with the ground reality on the fuzzy map, even though it has a lower AUC value. It is essential to mention that the minimum operator AND and Product create a region with low susceptibility, while the maximum operator OR and SUM maximize the susceptible area. They cannot achieve satisfactory performance in preparing a subsidence susceptibility map. Here, they have only been used to demonstrate the inefficiency of fuzzy operators in maximizing or minimizing subsidence susceptibility.

Due to the increasing need of human societies to groundwater, especially in arid and semi-arid regions such as Iran, Protection and prevention of pollution of these water resources are considered necessary. For this purpose, evaluating the vulnerability of groundwater can play a vital role in protecting and exploiting these resources. In the Barandozhay plain, due to the high agricultural activities, chemical fertilizers, pesticides application, and low depth of water table, there is the probability of aquifer contamination. For this purpose, at first, the potential of contamination of groundwater resources in the plain was studied using DRASTIC, SINTACS and SI models and then, the final map of vulnerable areas was prepared using the combination method. Comparison of the results obtained from the models with nitrate data based on the Correlation Index (CI) indicated that the combined method of the three models had more correlation than individual models of DRASTIC, SINTACS, and SI. Based on the combined method of the three models, 25, 40 and 35 percent of the Barandozchai plain aquifer area are located in the low, medium and high vulnerability range, respectively. Plain water is suitable for drinking according to the international standards of nitrates.

Nazloochay plain is located at north western of Urmia city in Iran. The approximate area of the plain is 508 Km2. It's located on the western coast of Urmia lake and is part of the Urmia basin. This research DRASTIC model was used to evaluate pollution vulnerability of aquifer of the region. DRASTIC model can be considered as the most common overlap index method used in these cases which considers seven hydrogeological parameters affecting ground water pollution including: Depth of ground water(D), net Recharge(R), Aquifer environment(A), Soil type(S), Topography(T), Impact of unsaturated zone(I) and Hydraulic conductivity(C). Combining these parameters of the model using GIS, indicated high pollution potential regions of aquifer and nitrate concentration has been applied to estimate the verification. DRASTIC vulnerability index of model was estimated between 38-153 for this case study, consisting of five ranges of pollution including: negligible; low (%44); little (33%); average (15%) and high (8%). According to designated vulnerability maps, the highest vulnerability potential is in central and southern section of the studying region. Based on nitrate concentration in pollution vulnerability shown in DRASTIC model, all area with high amount of nitrate were located in two categories of high and moderate ranges pollution susceptibility which confirms the model accuracy and preciseness.

Considering that a significant portion of water required for various purposes is supplied through groundwater resources in Iran, understanding the hydrogeochemical conditions and the factors influencing it as well as the concentration levels of dissolved salts and ions is of utmost importance. This study investigated the factors influencing the hydrogeochemical conditions of the Urmia aquifer in the Urmia Lake watershed. The study utilized a 15-year average of monthly hydrochemical analyses of water quality parameters up to the water year 2022-2023. Ion ratios, Gibbs and composite diagrams, and Piper and Chadha plots were employed for the analysis. Results from the ion ratios indicated that sodium ions in the Urmia aquifer have dual origins: direct and reverse ion exchange. It also showed that the Magnesium ions were derived from three sources: dolomite weathering, dolomite-limestone dissolution, and dolomite dissolution. Sulfate ions exhibited an anthropogenic origin, influenced by agricultural fertilizers and wastewater disposal. Additionally, results from ion ratios indicated that Lake Urmia and the associated saltwater intrusion had no significant impact on the groundwater quality of the studied aquifer. These findings were confirmed by Chadha diagrams. The nature of water-rock interaction reactions, as confirmed by Gibbs diagrams, was investigated in terms of direct and reverse ion exchange using the chloride alkalinity index and composite diagrams. Results showed that the reverse ion exchange dominated in the Shahrchay sub-basin, while in the other sub-basins the direct ion exchange prevailed. The presence of the Shahrchay reservoir in the Shahrchay sub-basin appears to have caused the hydrochemical behavior of groundwater to differ from other sub-basins. Geochemical modeling of the aquifer using saturation indices indicated a chemical environment conducive to the dissolution of halite, anhydrite, and gypsum (negative saturation index) and the precipitation of dolomite, calcite, and aragonite (positive saturation index). The use of Piper and Chadha diagrams revealed that calcium and magnesium bicarbonate types were the predominant water types and facies.

It is more expensive to remove pollution from groundwater than to prevent it. Delineation areas that arevulnerable to surface pollutants is one of methods to prevent pollution of groundwater resources. Focusing on this issue, DRASTIC model was used for evaluation of vulnerability of Tabriz-plain aquifer to pollution and the aquifer vulnerability map was prepared. The study shows that main zone of the aquifer’s groundwater is low tomodrate vulnerability to pollution (DRASTIC Index of 120-40) that consist of about 55.84% and areas with low, moderate to high, and high risk zones comprise 21.81, 22.08.% and 0.26% of the studied area, respectively Two tests of sensitivity analyses were carried out: the map removal and the single-parameter sensitivity analyses. Based on the characteristics of the studied area, the results from both map removal and single-parameter sensitivity analyses showed that the depth to water table has the most significant impact on the vulnerability risk zone. By overlaying of the vulnerability and landuse maps the areas where are subjected to potential release of pollutants from the agricultural activities were determined. Nitrate ion concentration and SINTACS model confirms the results of the vulnerability assessment.

Subsidence in the plains due to the over-exploitation of groundwater resources is one of the problems facing most of the country's plains. The drop in the water table of aquifers is the triggering factor for subsidence occurrence. However, due to the complexity of the nature of the problem, subsidence may not start immediately after the drop in the water table. In this study, a step has been taken in the direction of understanding the time lag in the water table drop in the occurrence of subsidence. The aquifer of Tasuj plain, as the study area, is located north of Lake Urmia in East Azerbaijan province. The amount of subsidence has been quantified by the INSAR technique in a differential manner in the plain. The research results also identify the vulnerable areas of this aquifer against subsidence. Due to the dynamic nature of the water table drop, which leads to the dynamics of the vulnerability results, the correlation of the vulnerability results with the INSAR results was calculated using the ROC curve and it was observed that the highest correlation was related to the water table drop two years ago of subsidence records. The subsidence occurs with a two-year time lag compared to the drop in the water table.

Excessive population growth over the past three decades, limited surface water resources, agricultural, industrial, urban activities and excessive exploitation of aquifers have caused irreparable damage to Iran''s water resources regarding quality and quantity. To prevent the continuation of quantitative and qualitative decline, management of exploitation and protection of groundwater should be the basis of planning in the country. Groundwater management requires an adequate understanding of the aquifer system and the use of a tool that can simulate the response to the various quantitative and qualitative stresses of the aquifer in the current and future conditions. In this regard, by using the model, real conditions in nature can be simulated with good accuracy and achieved to acceptable results. The study area of Gamasiab is located in Sahne-Bisotun plain in the northeast of Kermanshah province. In this area, only one case investigated in a small part of the aquifer. In this study, the status of the aquifer and the relationship between surface and groundwater in the entire aquifer zone of Sahneh-Bistoon plain simulated with the use of the MODFLOW 2000 code, the GMS v10 software. The results of the model show that the water budget of Sahneh-Bisotun plain is negative in the year 88-87 and is 22. 81 million cubic meters per year. The river is the main factor that recharges the aquifer, which is 248. 8 million cubic meters per year, and pumping from water wells is the most crucial factor in the drainage of aquifers, which is 36. 4 million cubic meters per year. The results of the model show that the Gamasiab River recharges the aquifer in two reaches and drains the aquifer in other reaches. The river drains 15. 55 million cubic meters of water per year from the aquifer.

Today, due to the importance of sustainable groundwater management, groundwater level modeling and forecasting are used to assess and evaluate water resources. The purpose of this study is to evaluate the performance of two models of Extreme Learning Machines (ELM) and Artificial Neural Network (ANN) and the combination of two models with wavelet transmission algorithms (W-ELM and W-ANN), which ultimately to increases the predictive power and optimization of input weights (the weights between the input and hidden layers) of models, Quantum Particle Swarm Optimization algorithm (QPSO) has been used. Also, in this study, the data of Ground Water Level of observation wells (GWL), precipitation (P) and average temperature (T) of Urmia Plain aquifer with a time series of 36 years (1981 – 2017) which were collected on monthly scale, are used. Also, in order to evaluate the performance of models, correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used. In this regard, 80% of the data (September 1981 to August 2010) are used for training section and 20% of data (September 2010 to August 2017) used for the test section of models. Based on the results of this study, the hybrid model of W-ELM-QPSO with correlation coefficient (R) 0. 991, 0. 983 and 0. 975, respectively for periods of one, two and three months in the test section, have a better performance than other models and also in addition to predicting power, this model has a high speed in terms of training and testing speed than other models.

Introduction: In countries with arid and semi-arid climates, such as Iran, studies of groundwater resources, the identification and management of aquifer systems, and the efficient and sustainable utilization of groundwater reserves_ recognized as one of the primary sources for meeting water demands&mdash, are of critical importance. In the other hand, the decline in groundwater resources caused by pollution and climate change has underscored the essential role of groundwater as a vital water supply across diverse climatic regions. Furthermore, the identification and quantification of groundwater, as well as its comparison with surface water resources, remain challenging due to the limited availability of reliable data and measurements. Methods: In this study, the quantitative and qualitative simulation and modeling of the SEMELQAN Plain aquifer, located in North Khorasan Province, were executed using the GMS 10 software and the MODFLOW model. Groundwater modeling can be applied in various forms,in the present research, groundwater flow was simulated using the MODFLOW software, which is based on the Taylor series expansion. The model was executed under steady-state conditions and in forward mode using the MODFLOW-2005 engine and due to uncertainties in some input parameters, relatively high errors were initially observed in the modeling results. Findings: In the final year, the inflow volume was approximately 6100 m³, /day, while the outflow volume was about 5600 m³, /day. These values show a decreasing trend over time, indicating a continuous reduction in the groundwater volume of the Semelqan aquifer. The relative RMS/RMSE error was 24. 1, demonstrating the high accuracy of the simulation. Its normalized value was 6%, which is below the acceptable threshold of 30% for long-term simulation periods.