Abstract
Groundwater plays a crucial role in sustaining industrial and agricultural production and meeting the water demands of the growing population in the semi-arid Guanzhong Basin of China. The objective of this study was to evaluate the groundwater potential of the region through the use of GIS-based ensemble learning models. Fourteen factors, including landform, slope, slope aspect, curvature, precipitation, evapotranspiration, distance to fault, distance to river, road density, topographic wetness index, soil type, lithology, land cover, and normalized difference vegetation index, were considered. Three ensemble learning models, namely random forest (RF), extreme gradient boosting (XGB), and local cascade ensemble (LCE), were trained and cross-validated using 205 sets of samples. The models were then applied to predict groundwater potential in the region. The XGB model was found to be the best, with an area under the curve (AUC) value of 0.874, followed by the RF model with an AUC of 0.859, and the LCE model with an AUC of 0.810. The XGB and LCE models were more effective than the RF model in discriminating between areas of high and low groundwater potential. This is because most of the RF model’s prediction outcomes were concentrated in moderate groundwater potential areas, indicating that RF is less decisive when it comes to binary classification. In areas predicted to have very high and high groundwater potential, the proportions of samples with abundant groundwater were 33.6%, 69.31%, and 52.45% for RF, XGB, and LCE, respectively. In contrast, in areas predicted to have very low and low groundwater potential, the proportions of samples without groundwater were 57.14%, 66.67%, and 74.29% for RF, XGB, and LCE, respectively. The XGB model required the least amount of computational resources and achieved the highest accuracy, making it the most practical option for predicting groundwater potential. The results can be useful for policymakers and water resource managers in promoting the sustainable use of groundwater in the Guanzhong Basin and other similar regions.
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The datasets used or analyzed and the Python code during the current study are available from the corresponding author on reasonable request.
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Funding
The study was supported cooperatively by Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0805-02), Key deployment projects of the Chinese academy of sciences (ZDRW-ZS-2020–3), National Natural Science Foundation of China (U20A2088), Innovation Team Foundation of Qinghai Office of Science and Technology (2022-ZJ-903), and Kunlun Talented People of Qinghai Province, High-end Innovation and Entrepreneurship talents-Leading Talents (E140DZ3901).
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Zitao Wang contributed to the study’s methodology, performed formal analysis, drafted the original manuscript, and created visualizations. Jianping Wang conceptualized the study, reviewed and edited the manuscript. Dongmei Yu was responsible for data curation. Kai Chen contributed to the validation of the study’s findings. All authors reviewed and approved the final manuscript for submission.
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Highlights
• The groundwater potential in the Guanzhong Basin was assessed using four ensemble learning models: Random Forest (RF), Extreme Gradient Boosting (XGB), and Local Cascade Ensemble (LCE).
• The XGB model was found to be the most accurate, with an AUC value of 0.874, followed by the RF model with an AUC of 0.859, and the LCE model with an AUC of 0.810.
• The XGB model required the least amount of computational resources and achieved the highest accuracy, making it the most practical option for predicting groundwater potential in the study area.
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Wang, Z., Wang, J., Yu, D. et al. Groundwater potential assessment using GIS-based ensemble learning models in Guanzhong Basin, China. Environ Monit Assess 195, 690 (2023). https://doi.org/10.1007/s10661-023-11388-2
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DOI: https://doi.org/10.1007/s10661-023-11388-2