Integrated machine learning and remote sensing for groundwater potential mapping in the Mekong Delta in Vietnam

IF 2.3 4区 地球科学 Acta Geophysica Pub Date : 2024-04-03 DOI:10.1007/s11600-024-01331-5
Huu Duy Nguyen, Quoc-Huy Nguyen, Dinh Kha Dang, Tien Giang Nguyen, Quang Hai Truong, Van Hong Nguyen, Petre Bretcan, Gheorghe Șerban, Quang-Thanh Bui, Alexandru-Ionut Petrisor
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Abstract

Evaluating groundwater potential is critical for the socioeconomic development of Vietnam. This research aims to assess the underground water potential in the country’s Mekong Delta using the machine learning (ML) such as support vector machines (SVM), CatBoost (CB), K-nearest neighbors (KNN), random forest (RF) and AdaBoost (ADB). The problem of exploitation of groundwater resources in the delta is aggravated due to global warming and growth of population. In total, 146 groundwater points and 14 drivers (namely elevation, aspect, curvature, slope distance to river and river density, land use, normalized difference built-up index, flow accumulation, rainfall, soil type, normalized difference vegetation index, stream power index, terrain roughness index, and topographic wetness index) were used to assess groundwater potential. Each proposed model was evaluated utilizing area under curve (AUC), root mean square error, coefficient of determination (R2), and mean absolute error. The findings showed that the RF outperformed the others in building of a groundwater potential map. In which, AUC value was estimated at 0.99 and R2 value was estimated at 0.63 then came CB (AUC = 0.98, R2 = 0.56), ADB (AUC = 0.92, R2 = 0.50), SVM (AUC = 0.91, R2 = 0.57), and KNN (AUC = 0.75, R2 = 0.45). The results illustrate the power of ML in assessing groundwater potential and can support decision makers, planners, and local authorities responsible for sustainable groundwater planning in the Mekong Delta and beyond.

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综合机器学习和遥感技术绘制越南湄公河三角洲地下水潜力图
评估地下水潜力对越南的社会经济发展至关重要。本研究旨在利用支持向量机(SVM)、CatBoost(CB)、K-nearest neighbors(KNN)、随机森林(RF)和 AdaBoost(ADB)等机器学习(ML)技术评估越南湄公河三角洲的地下水潜力。由于全球变暖和人口增长,三角洲的地下水资源开发问题日益严重。共使用了 146 个地下水点和 14 个驱动因素(即海拔、坡度、曲率、坡距河流距离和河流密度、土地利用、归一化差异建筑指数、流量累积、降雨量、土壤类型、归一化差异植被指数、溪流动力指数、地形粗糙度指数和地形湿润度指数)来评估地下水潜力。利用曲线下面积 (AUC)、均方根误差、判定系数 (R2) 和平均绝对误差对每个建议模型进行了评估。研究结果表明,在绘制地下水潜势图方面,RF 的表现优于其他模型。其中,AUC 值估计为 0.99,R2 值估计为 0.63,然后是 CB(AUC = 0.98,R2 = 0.56)、ADB(AUC = 0.92,R2 = 0.50)、SVM(AUC = 0.91,R2 = 0.57)和 KNN(AUC = 0.75,R2 = 0.45)。这些结果表明了 ML 在评估地下水潜力方面的能力,可为决策者、规划者和负责湄公河三角洲及其他地区可持续地下水规划的地方当局提供支持。
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来源期刊
Acta Geophysica
Acta Geophysica GEOCHEMISTRY & GEOPHYSICS-
CiteScore
3.80
自引率
13.00%
发文量
251
期刊介绍: Acta Geophysica is open to all kinds of manuscripts including research and review articles, short communications, comments to published papers, letters to the Editor as well as book reviews. Some of the issues are fully devoted to particular topics; we do encourage proposals for such topical issues. We accept submissions from scientists world-wide, offering high scientific and editorial standard and comprehensive treatment of the discussed topics.
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