A study on the establishment of groundwater protection area around a saline waterway by combining artificial neural network and GIS-based AHP

Abstract

Because the mixture of seawater and freshwater in the Gyeongin-Ara Waterway in South Korea can lead to the intrusion of saline water into surrounding aquifers, systematic management through the establishment of a groundwater protection area is required. The analytic hierarchy process (AHP) model is used to delineate this protection area based on two primary factors and five secondary factors related to saline water intrusion. The study area is divided into 987 gridded cells with a unit size of 100 × 100 m, and the final evaluation score for each cell is calculated using the AHP model. Consequently, several artificial neural network models based on a multilayer perceptron are developed using the AHP’s secondary criteria and the evaluation score. Comparing the evaluation scores of ANN and AHP, more than 180 samples are required in the ANN model to insure high R2 between the original and estimated values. The ANN model is more consistent than the AHP model when determining groundwater protection area, because it can be re-constructed due to the changes in some secondary criteria and also changed due to a standardization process. The final evaluation score by the ANN model based on 300 samples, with the highest R2, is calculated and the regions with a score higher than 2.0 are selected as the groundwater protection area, accounting for 15% of the total cells. This area is similar to the range within approximately 200 m of the GA Waterway and also includes some changing sites in hydrogeochemistry and electric conductivity, which is produced by saline water intrusion. If the land-use type, groundwater levels, and some other criteria change at any cell, the ANN model can be re-executed to verify whether the cell belongs to a groundwater protection area. Considering that salinity of groundwater near the waterway can be affected by various factors including well depth, pumping conditions, and groundwater levels, the ANN model, which is a non-linear model, can be more effective for prediction than the AHP model.