Development of an Entropy Method for Groundwater Quality Monitoring Network Design

Abstract

A new approach is presented based on the entropy theory to design an optimal groundwater quality monitoring network. First, the unrecorded values of Total Dissolved Solids (TDS) are estimated in the study area using Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) methods, and then, using Differential Evolution (DE) optimization algorithm aimed at maximizing the pure information gained by stations. The number and location of monitoring stations are selected optimally from the active wells located in the study area. The spatial distribution of chosen wells provides adequate coverage of the whole aquifer and, at the same time, gives the maximum useful information about the water quality status. It also avoids selecting any redundant station considering the operational and temporal costs. The proposed entropy method is compared with “error minimization” and “K-Means clustering” methods and is shown to be superior. The proposed approach is used for optimizing Eshtehard aquifer monitoring network in central part of Iran. As a result of its implementation, 20 wells are selected among 79 active monitoring wells in the study area, so that by sampling the wells, the maximum qualitative information can be obtained without needing to sample all the wells.