Evaluating the effectiveness of artificial neural network and multivariate regression methods in investigating the drawdown in the level of aquifers (case study: Arak plain aquifer)

In recent years, the groundwater resources of Arak plain have been under severe stress, so in some areas, due to the drying up of wells, they have increased the depth of wells to access water. In some areas, the groundwater depth (GWD) is high, which will lead to the salinization of those lands in the future. Regional modeling was used to organize and measure the response of the groundwater resources of Arak plain against the implementation of different management and implementation scenarios. This study aims to investigate the effective factors in the GWD to provide a regional model with multiple linear regression (MLR) method for Arak plain aquifer. For this purpose, the average GWD in the Arak plain, as a dependent variable, and the transmissivity of the aquifer formations, groundwater exploitation values, altitude, average precipitation of the region, the amount of evaporation, and the distance from water resources  are considered independent variables and regression analysis is done in SPSS software media. It was done to present a linear model. In the next stage, the presented model was evaluated by applying it to places where its statistics and information were not used to present the model, and finally, by applying this model in the GIS environment, the GWD map for the region was created. The study was prepared. Also, an artificial neural network (ANN) was used to simulate the depth of underground water. The performance of the ANN was measured through parameters such as root mean square error (RMSE) and correlation coefficient between real and desired outputs (R). The results of both methods indicate that factors such as the transmissivity of aquifer formations, GWD drawdown, topography (the height of the well site on the level of the watershed), the groundwater exploitation values ​​at the maximum operating radius of the well, and the distance from water resources are the main factors of GWD drawdown. But the effectiveness of ANN in estimating GWD drawdown is higher than the MLR method.