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Understanding annual extraction volumes and fluctuations in groundwater depth is essential in water resource management. This study applied various machine learning methods to predict the spatial variability of groundwater depletion (GWD) in an alluvial aquifer located along the southern coasts of the Caspian Sea (Mazandaran Plain). Initially, mean GWD values were measured from 250 piezometric wells across the plain. Subsequently, the factors controlling GWD were identified and provided. These factors encompass natural elements, including precipitation, evaporation, topography, groundwater depth, aquifer transmissivity, and proximity to water bodies. They also include anthropogenic factors such as extraction rates and proximity to industrial centres. Three machine learning models – Random Forest (RF), Deep Learning (DL), and Extreme Gradient Boosting (EGB) – were employed to model GWD using consistent training and test data. In this context, GWD was used as the output, and its controlling factors were incorporated as input variables. Correlation coefficients between GWD and its controlling factors revealed that the transmissivity of aquifer formations (R = 0.81), groundwater withdrawal within well radii (R = 0.69), and groundwater depth (R = 0.6) were the most significant factors. All three models demonstrated high efficacy during the training and testing stages. However, during testing, EGB offered the highest performance in modelling GWD due to extraction (R2 = 0.8, NSE = 0.82). The verified model was then used to predict annual drawdown or extraction across the plain. The predicted results were visually represented as an annual GWD or extraction map within a GIS framework. A comparison between observed GWD values in piezometric wells and the predicted values confirmed the robust performance of the proposed methodology (R2 = 0.8). This methodology can be employed effectively to predict spatial changes in GWD or annual extraction in other plains. [ABSTRACT FROM AUTHOR]

Dendrohydrogeology can be used to simulate historical groundwater depth, water table drawdown, groundwater recharge and piezometric lines. We simulated paleohydrogeologic conditions via tree-rings and vessel chronologies using an artificial neural network (ANN) in the alluvial aquifer of the Caspian southern coast of Iran during the past century. Tree-ring width, vessel features, secondary piezometric well data, and precipitation from different sites within the study area were evaluated. After cross-dating, standardization and time series analysis, the relationships between tree-rings and vessel chronologies with groundwater depth were defined and simulated. Additionally, paleohydrogeologic records during the past century were simulated. The results generally demonstrate that tree-ring width is a better index than vessel features. However, we obtained the most exact groundwater depth modeling results by using the combination of tree-rings and earlywood vessel diameter from periods of low precipitation and groundwater fluctuations and significant temperature fluctuations. We also found that dendrohydrogeology has more applicability in groundwater modeling in areas where groundwater depth fluctuates 10–20 m below ground surface (based on root depth and water access). Moreover, using the simulated groundwater depths, piezometric lines in 1927 and 2000 (the years with maximum natural recharge and maximum drawdown respectively) were extracted using an interpolation technique and Geographic Information System (GIS). Finally, we suggest applying dendrohydrogeology for paleohydrogeologic modeling in alluvial aquifers. [ABSTRACT FROM AUTHOR]