Search

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]

Summary Groundwater is the most important water resource in semi-arid and arid regions such as Iran. It is necessary to study groundwater level fluctuations to manage disasters (such as droughts) and water resources. Dendrochronology, which uses tree-rings to reconstruct past events such as hydrologic and climatologic events, can be used to evaluate groundwater level fluctuations. In this study, groundwater level fluctuations are simulated using dendrochronology (tree-rings) and an artificial neural network (ANN) for the period from 1912 to 2013. The present study was undertaken using the Quercus Castaneifolia species, which is present in an alluvial aquifer of the Caspian southern coasts, Iran. A multilayer percepetron (MLP) network was adopted for the ANN. Tree-ring diameter and precipitation were the input parameters for the study, and groundwater levels were the outputs. After the training process, the model was validated. The validated network and tree-rings were used to simulate groundwater level fluctuations during the past century. The results showed that an integration of dendrochronology and an ANN renders a high degree of accuracy and efficiency in the simulation of groundwater levels. The simulated groundwater levels by dendrochronology can be used for drought evaluation, drought period prediction and water resources management. [ABSTRACT FROM AUTHOR]