An Evaluation of the Capability of the NARX Neural Network in Predicting Ground Water Level Changes

Abstract

The efficient monitoring and tracking of groundwater level changes are critical for the sustainable management of water resources, especially in light of population growth and climate change. This study evaluates the ability of the Non-linear Autoregressive with exogenous input (NARX) model to simulate groundwater level trends in Ajabshir, Iran, using groundwater level data from 2006 to 2019 as the baseline period. The model was trained using time, groundwater levels, and delay times between 1 and 2 as the input training samples. The results indicate that the NARX model performed exceptionally well in simulating historical trends of groundwater levels, achieving a Coefficient of Determination (DC) value of 0.87 and a Root Mean Squared Error (RMSE) of 0.215.

Keywords

• groundwater levels
• non-linear autoregressive with exogenous input (NARX)
• simulation
• sustainable water management