A Comprehensive Review on Mapping of Groundwater Potential Zones: Past, Present and Future Recommendations

Choudhary, Sourav; Jain, Jagriti; Pingale, Santosh Murlidhar; Khare, Deepak

doi:10.1007/978-3-031-35279-9_6

Cited by 2 publications

(1 citation statement)

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“…One of the successful approaches to reduce the detrimental effects of excessive groundwater use is to map the groundwater potential, which indicates how much water an aquifer can store and deliver. This approach takes into account a variety of factors, including aquifer properties, topography, climate, land cover, geology, and hydrology, to spatially identify areas with high or low groundwater potential (Choudhary et al, 2023;Thanh et al, 2022). By implementing this approach, groundwater potential mapping (GPM) can facilitate more effective management and conservation of groundwater resources, ultimately reducing the negative impacts of excessive groundwater usage (Guo et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Le,

Tian

et al. 2024

Earth and Space Science

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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.

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Section: Introductionmentioning

confidence: 99%