Year: 2025
Type: article
Abstract: Groundwater pollution presents a serious concern in arid and semiarid regions, where water resources are already limited. In such contexts, reliable and efficient methods for assessing groundwater vulnerability are critical. Without adequate knowledge of the vulnerability, groundwater is at greater risk of severe contamination. This not only threatens the availability of clean water but also demands significant time and financial resources for remediation and restoration. Modelling groundwater vulnerability is even more demanding and complex in data-scarce regions. Consequently, this study investigates and predicts spatial variations in groundwater quality and vulnerability within a data-scarce area by applying efficient machine learning methods that compensate for the limited availability of quality groundwater data. Supportive machine learning approaches such as bagged adaptive boosting (BAB), averaged neural network (avNNet), heteroscedastic discriminant analysis (HAD), rotation forest (RotationF), and an ensemble method were applied to assess groundwater vulnerability using k-fold cross-validation. The results demonstrate that the BAB model achieved the best performance, with both accuracy and precision exceeding 85%. Furthermore, the stacking ensemble approach, specifically the BAB model combination, increased precision by 4% and reduced false alarms by 6%. The most influential variables affecting groundwater quality include groundwater depth, precipitation, proximity to waterways and roads, topographic humidity, and the percentage of fine-grain material. The results also show that variability in the data significantly impacts the modelling performance.
Institutions: Korea University, Korea University, Hongik University, University of Hawaiʻi at Mānoa, University of South Africa, National Yunlin University of Science and Technology, National University of Malaysia, University of Tehran, Obuda University, Ludovika University of Public Service, National Pingtung University, Soil Conservation and Watershed Management Research, Agricultural Research & Education Organization
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,790
Funders: National Science and Technology Council, National Research Foundation, Ministry of Environment, National Research Foundation of Korea, Ministry of Education, India, Ministry of Science and ICT, South Korea, National Science and Technology Council, Korea Environmental Industry and Technology Institute
OpenAlex ID: W4409774371