Optimization of groundwater monitoring networks using multi-criteria decision-making (MCDM) and principle component analysis (PCA) models

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Chinese explanation / 中文解读

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Original abstract

Monitoring groundwater is essential to understand its quantitative and qualitative properties. This study aims to optimize the quantitative and qualitative monitoring networks of groundwater resources in the Fotuiyeh-Tedruiyeh aquifer in southern Iran. Initially, nine criteria were selected, including the long-term average groundwater level, long-term annual groundwater decline, hydraulic conductivity, density of extraction wells, distance from springs and Qantas, distance from rivers, geological formations, land use, and distance from faults. Each criterion was weighted using the analytic hierarchy process (AHP) method, and then two multi-criteria decision-making (MCDM) models consisting of the weighted aggregated sum product assessment (WASPAS) and the technique for order of preference by similarity to ideal solution (TOPSIS) were used for the location process. Subsequently, the current monitoring network was evaluated against the location results, groundwater data homogeneity tests were conducted in unsuitable wells, and the principle component analysis (PCA) method as a data mining model was used to determine the relative importance of the wells. The results indicated that both WASPAS and TOPSIS models provided consistent and accurate results. Out of 31 wells, 4 were identified as unsuitable. Clustering of the wells by PCA was then performed, and by removing less important wells, the standard error was reduced. Therefore, considering the effectiveness of the methods used in this study, their application is recommended for other plains in Hormozgan Province.

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