Novel Ensemble Models Based on the Split‐Point Sampling and Node Attribute Subsampling Classifier for Groundwater Potential Mapping

• Published in: Earth and space science (Hoboken, N.J.) Vol. 11; no. 6
• Main Authors: Wang, Zhengtao, Le, TienDuy, Tian, Kunjun, Phong, Tran Van, Bien, Tran Xuan, Pham, Binh Thai
• Format: Journal Article
• Language: English
• Published: Hoboken John Wiley & Sons, Inc 01.06.2024; American Geophysical Union (AGU)
• Subjects: Accuracy; Agricultural production; Agriculture; Aquifers; bagging; Climate change; Efficiency; ensemble modeling; GIS; Groundwater; Groundwater potential; Hydroelectric power; Land use; Machine learning; MultiBoostAB; Neural networks; random subspace; Regression analysis; River basins; Rivers; Sesan River basin; Statistical methods; Water resources management; Water shortages; Water use; Cambodia; Iran; Vietnam
• ISSN: 2333-5084; 2333-5084
• DOI: 10.1029/2023EA003338

Groundwater potential maps are crucial tools for effectively managing water resources, particularly in agriculturally focused countries such as Vietnam. However, creating these maps is a challenging task that requires reliable data and methods. In this study, we integrated the Split‐Point Sampling and Node Attribute Subsampling Classifier (SPAARC) with the Bagging (B), MultiBoostAB (MBAB), and Random Subspace (RSS) ensemble learning techniques and developed three ensemble models: B‐SPAARC, MBAB‐SPAARC, and RSS‐SPAARC. We selected 13 geoenvironmental factors based on their availability, relevance, and association with groundwater potential in the Sesan River basin of Vietnam. We assessed the models' performance using various metrics such as area under the curve (AUC), accuracy, sensitivity, specificity, and RMSE. The findings indicated that the ensemble models performed better than the single SPAARC model in mapping groundwater potential. The MBAB‐SPAARC model demonstrated the highest accuracy with an AUC value of 0.891, followed by B‐SPAARC (AUC = 0.844), RSS‐SPAARC (AUC = 0.871), and the single SPAARC (AUC = 0.853) models. The results also highlighted that elevation, rainfall, land use/cover, and altitude were the most significant factors for mapping groundwater potential in the Sesan River basin. The innovative ensemble models and reliable potential maps developed in this study assist water resource managers in planning water usage based on the benefits and costs for various users and in devising sustainable strategies for using, protecting, and managing groundwater. Key Points Groundwater potential mapping was carried out at the Central Highlands of Vietnam Hybrid Machine learning Models based on Naïve Bayes Tree were developed and used Results showed that the proposal models are potential and accurate tools