Compare IBK and M5 Models with Ground-Based Methods in Underground Water Quality

In groundwater management, one of the crucial and fundamental aspects is estimating the groundwater level, which is typically done based on data obtained from monitoring well networks. In this research, the main objective was to investigate the groundwater salinity in the Mashhad Plain during the period of 1st to 31st of April, 2019. For this purpose, two statistical models, IBK and M5, were used, and their results were compared with geostatistical methods. Point sampling with geographic coordinates X and Y was employed to examine the groundwater salinity. Additionally, chloride was considered as an important variable and indicator of groundwater salinity due to its significant correlation with electrical conductivity, which is indirectly related to groundwater salinity. The results showed that the IBK model outperformed the M5 model and geostatistical methods. By using the IBK model, the accuracy in predicting groundwater salinity significantly improved. Based on the obtained statistical values, it can be concluded that the IBK model, with an R-squared value of 0.98, a root mean square error (RMSE) of 85.200, and a mean absolute error (MAE) of 48.97, performed the best in predicting groundwater salinity in the Mashhad Plain. Thus, the IBK model serves as an effective tool for accurate estimation of groundwater salinity and quantitative groundwater management. Its high performance enables better decision-making and implementation of appropriate measures for preserving and optimizing groundwater resources. These findings can be valuable for managers.