Prediction of groundwater quality index to assess suitability for drinking purpose using averaged neural network and...

Groundwater quality management is pivotal for ensuring public health and ecological resilience. However, the conventional water quality indices often face challenges related to parameter selection, geographic coverage, and scalability. The integration of machine learning and spatial analysis represents a promising methodological shift, allowing for high accuracy and adaptive management strategies. The Safe Groundwater Project in Unsupplied Areas (2017-2020) employed a comprehensive Groundwater Quality Index (GQI) to evaluate potable groundwater quality across South Korea, utilizing a large dataset comprising 28 water quality parameters and 3552 wells. This study revealed that over 50 % of the evaluated wells (Total 8326 wells) were inappropriate as sources of drinking water, indicating a pressing need for policy revision. The averaged neural network model achieved a high predictive accuracy of approximately 95 % for GQI grades, outperforming other classification models. The introduction of 2D spatial analysis in conjunction with machine learning algorithms notably increased the predictive accuracy for unevenly distributed groundwater samples. Moreover, this combined approach enabled the intuitive visualization of groundwater vulnerability across various regions, which can inform targeted interventions for effective resource allocation and management. This research represents a methodologically robust, interdisciplinary approach that holds significant implications for a framework for future groundwater quality management and vulnerability assessment.