Background and Objectives: Gorgan Bay, as one of the unique aquatic ecosystems in northern Iran, has faced serious environmental challenges in recent years. These challenges stem from various factors, including climate change, decreasing water levels of the Caspian Sea, increased human activities, and morphological changes in the region. Accurately identifying the factors leading to the degradation of this ecosystem is essential for sustainable water resource management and environmental protection. This study aims to analyze changes in water surface area and water quality in Gorgan Bay from 2000 to 2023 and identify the factors influencing these changes.Materials and Methods: This research analyzes changes in water surface area and water quality in Gorgan Bay from 2000 to 2023 using satellite data from Landsat, MODIS, Sentinel, and Jason. Indicators such as water temperature, turbidity, and CDOM (Colored Dissolved Organic Matter) were examined to assess water quality in the region. Additionally, water surface area maps of the bay were created using MNDWI (Modified Normalized Difference Water Index) and NDWI (Normalized Difference Water Index). Pearson correlation coefficients (r) were utilized to analyze the relationship between meteorological and satellite parameters and the water surface area of the bay, identifying linear correlations among these variables. Shapley diagrams were employed to analyze feature importance and clarify complex impact patterns on the bay's surface area. A linear regression model was also applied to evaluate the linear relationship between input variables and the bay's surface area. Finally, to analyze the impact of factors such as the water level of the Caspian Sea, the area of the Caspian Sea, precipitation, temperature, and inflow discharge on the bay's surface area, a random forest model was utilized.Results: The results indicate that from 2015 to 2023, the minimum water temperature in Gorgan Bay increased by an average of 2.3°C, primarily observed in the southern and western regions of the bay. Furthermore, there has been a continuous increase in water turbidity in recent years, particularly in 2020, 2022, and 2023, reaching unhealthy levels in the western and southern areas of the bay. The CODM index for Gorgan Bay in 2020, 2022, and 2023 remained in a suitable condition, with no significant pollution detected. In 2015, the water quality was higher than in recent periods but still did not reach unhealthy levels, with only small portions of the western areas approaching unhealthy conditions. The MNDWI and NDWI indices indicate that over 50% of the initial surface area of Gorgan Bay has been lost over these years. The analysis shows a significant reduction in the bay's surface area, particularly in 2020, 2022, and 2023, with large areas of the western, southern, and northern parts of the bay completely dried up. The study found that the decrease in the water level of the Caspian Sea is the most critical factor contributing to the drying of Gorgan Bay. The Pearson correlation coefficient between the bay's surface area and the Caspian Sea water level was calculated to be less than -0.90, with a coefficient of determination of 0.82, indicating a strong inverse relationship between these two variables. The relationships of other parameters, including the water area of the Caspian Sea, inflow discharge, precipitation, and temperature with the surface area of Gorgan Bay, were determined with coefficients of determination of 0.43, 0.40, 0.19, and 0.11, respectively, indicating that temperature has the least impact on the reduction of the bay's surface area. Additionally, the Shapley coefficient revealed that the water level of the Caspian Sea had the greatest variability across the horizontal axis, indicating its role in the surface area of Gorgan Bay. Other examined parameters, such as inflow discharge, the area of the Caspian Sea, temperature, and precipitation, also played significant roles in this process. Regression analysis to assess the role of the examined parameters on the reduction of Gorgan Bay's surface area based on coefficient values indicated that the water level of the Caspian Sea (coefficient of 0.54) had a more significant role in the drying of Gorgan Bay compared to other variables. In fact, the water level of the Caspian Sea had more than 50% greater influence on the drying process of Gorgan Bay than other features. The feature importance analysis using the random forest method showed that the water level of the Caspian Sea had a coefficient of 0.78, the area of the Caspian Sea had a coefficient of 0.14, while inflow discharge, temperature, and precipitation had coefficients of less than 0.1, indicating their lesser impact on the drying of Gorgan Bay. Moreover, the analysis of the water level of the Caspian Sea and the surface area of Gorgan Bay from 2000 to 2023 revealed a decrease in the bay's water surface area from 400 square kilometers in 2000 to 260 square kilometers in 2023, closely related to a decrease in the Caspian Sea water level, which has dropped by over 2 meters in the past 23 years. These changes are directly linked to the regional morphology of Gorgan Bay, particularly the reduction in depth and changes in the coastal shape, which have exacerbated the drying process and reduced water surface area.Conclusion: The findings of this study indicate that Gorgan Bay has faced serious challenges from 2000 to 2023 due to declining water levels and water quality. Correlation analyses and regression models demonstrated that the water level of the Caspian Sea plays a primary role in these changes, with a direct and significant relationship between the decrease in water levels and the reduction of the bay's surface area. The results highlight the necessity for sustainable water resource management and the protection of Gorgan Bay as a sensitive ecosystem facing declining water levels and changes in water quality.

Introduction: Aquifers are the major source of freshwater in many parts of the world. Saltwater intrusion )SWI) is a serious environmental issue since 80% of the world’ s population live along the coast and utilize local aquifers for their water supply. Globally, coastal aquifers are under threat from saltwater intrusion (SWI). SWI is caused by changes in coastal aquifer conditions resulting from ground water extraction, climate drivers, sea-level rise, oceanic over topping events, and land use change. Under natural conditions, these coastal aquifers are recharged by rainfall events, and the regional groundwater flow towards the ocean counters the intrusion of saltwater into the freshwater region. However, over-exploitation of coastal aquifers in some regions has resulted in a reduction in fresh groundwater levels (and hence reduced natural flow) and this has led to an increase in saltwater intrusion. Saltwater intrusion degrades the quality of coastal aquifer groundwater resource which can lead to a reduction in crop yield efficiency, limitation on the drinking water resource as well as soil fertility and salinity of operated wells. Such problems are more crucial where groundwater aquifers are shallow. Materials and Methods: The aim of this study is to investigate the effect of Lake Urmia water-level fluctuations on groundwater table and rate of the intruding or receding of salt water to the coastal aquifer. In order to achieve this purpose, Rashakan coastal aquifer in the vicinity of Lake Urmia in the northwest of Iran was simulated. In this study, SEAWAT model was used to simulate the problem. SEAWAT was specifically designed for the simulation of SI, although it has many other applications as well, notably the combined simulation of groundwater flow and heat transfer. SEAWAT as a widely used, three-dimensional variabledensity groundwater flow and transport model has been developed by the USGS based on MODFLOW and MT3DMS and includes two additional packages: Variable-Density Flow (VDF) and Viscosity (VSC). First, the model was calibrated and then the simulations were defined in four scenarios as follows: a) The rate of the intruding or receding of salt water to the coastal aquifer during recent years b) The effect of the drop-in lake water level on groundwater level changes trend regardless of changes in lake water density c) The effect of the drop-in lake water level on groundwater level changes trend in view of changes in lake water density d) The effect of the drop-in lake water level on the rate of the intruding or receding of salt water. Results and Discussion: In this study, simulations were carried out under four scenarios in order to investigate the effect of Lake Urmia water-level fluctuations on groundwater table and rate of the intruding or receding of salt water to the coastal aquifer. In the first scenario, in order to assess the rate of the intruding or receding of salt water to the coastal aquifer in recent years, three profiles have been investigated in the north and the center and south of the study area, and the results showed that in recent years there has been no significant change in the displacement of the salt-water wedge and this change was less than 50 meters and only the upper part of wedge connected to the lake was more affected by dropping water level of lake, which was due to retreat of the boundary imposed by lake water recession. In the second scenario, the effect of the drop-in lake water level on groundwater level changes trend, regardless of changes in lake water density, was investigated. The findings of the study showed that if the concentration of lake water is considered constant, the increase and decrease in groundwater level across the aquifer would be almost equal to the increase and decrease the lake water level. In the third scenario, the effect of the drop-in lake water level on groundwater level changes trend in view of changes in lake water density was investigated and the findings was also made with the second scenario, where the results showed that when the effect of the density changes is neglected, the groundwater level is affected by the lake water level changes more than about 2 times that when the density changes are considered. However, increasing and decreasing concentrations, and consequently increasing and decreasing the density, may have a great effect on the reduction and increase of groundwater levels. In the fourth scenario, the effect of the drop-in lake water level on the rate of the intruding or receding of salt water was also investigated. It can be concluded that when concentration changes and as a result of variations in density are affected, by decreasing the level of the lake, saltwater wedge would be intruded and when the effect of the density changes is neglected, saltwater wedge would be receded. Conclusion: The results of this study indicated that during recent years there has been no significant change in the location of the salt water wedge, and this change is less than 50 meters. The upper part of wedge connected to the lake is more affected by dropping water level of lake, which is due to retreat of the boundary imposed by lake water recession. Also, the findings of the study showed that if the concentration of lake water is considered constant, the increase and decrease in groundwater level across the aquifer will be almost equal to the increase and decrease of the lake water level. When the effect of the density changes is neglected, the groundwater level is affected by the lake water level changes more than by about 2 times that when the density changes are considered. Despite the decreasing of about 7 meters of lake Urmia water level, due to increase the density of water, the wedge has intruded. This research shows that in the event of an increase in the water level of Lake and consequently a decrease in water density, Saltwater wedge would be receded.

In recent decades, drought and weak management of water resources has caused many lakes and wetlands to enter critical conditions. Surface water level prediction, although an important and complex hydrological process, is vitally required for better management and improvement of such ecosystems. In this research, four soft-computing techniques including Wavelet Artificial Neural Network (WANN), Artificial Neural Network (ANN), Adaptive-Neuro-Fuzzy Inference System (ANFIS) and Gene Expression Programming (GEP) were used to predict 2-month water level fluctuations of Zarebar Lake. The predicted water levels in each technique were compared with observed data and statistical indicators; RMSE, MAE, and R2, were used to evaluate the performance of each method. The results proved that WANN performed considerably better and its prediction was more accurate followed by ANFIS, GEO and ANN, in regards of accuracy corresponded to observed data. The selected technique in this research can be recommended for prediction of the water levels in lakes and wetlands with significant accuracy.

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.

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.

The hydrological and geomorphological changes in catchments is one of the most important challenges today. Analysis of hydrological time series such as groundwater level plays an important role in the behavior identification of them against various factors. In this study, the effect of wavelet based de-noising on the entropy of groundwater level time series in Ardabil plain has been investigated. Also, the effective sub-series of the groundwater level time series process were identified using three criteria: entropy, mutual information (MI) and linear correlation coefficient. The results showed that the entropy of the groundwater level time series increased using wavelet based de-noising method. The increase of entropy indicates an increase natural fluctuations in the groundwater level time series and thus indicates the occurrence of a favorable trend in it. Also, the results showed that MI and entropy criteria, due to nonlinear nature, can accurately demonstrate the dominant sub-series in the groundwater level process. A+D3 combination was considered as the dominant sub-series in most piezometers.