Assessment of Supervised Machine Learning Techniques for Predicting Groundwater Availability in Mexican Aquifers

被引:0
作者
Sanchez, Alberto Gonzalez [1 ]
Capurata, Ronald Ernesto Ontiveros [1 ]
机构
[1] Inst Mexicano Tecnol Agua, Coordinac Seguridad Hidr, Jiutepec, Morelos, Mexico
来源
INTERNATIONAL JOURNAL OF COMBINATORIAL OPTIMIZATION PROBLEMS AND INFORMATICS | 2024年 / 15卷 / 04期
关键词
aquifers overexploitation; machine learning; support vector machine regression; artificial neural networks; M5'; random forests regression; REGRESSION;
D O I
10.61467/2007.1558.2024.v15i4.498
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Groundwater overexploitation is a global problem. In Mexico, 653 aquifers provide 39.1% of water for consumptive use. The National Water Commission manages this resource, but predicting aquifer availability is challenging, and the number of aquifers in deficit has increased. Physical models can address this issue but require extensive resources, whereas supervised learning algorithms offer a less resource-demanding alternative. This study evaluates four machine learning techniques for groundwater availability prediction: support vector machine regression, M5' model trees, random forests (RFR), and artificial neural networks. The models were trained using climatological, land use, and concession data from 1997 to 2015 and tested with data from 2018 and 2020. Random forests performed the best, showing a high correlation coefficient and low RMSE errors. The prediction accuracy for the availability state was 81.24% for 2018 and 76.79% for 2020. Thus, RFR can effectively predict short-term water availability, aiding sustainable aquifer management.
引用
收藏
页码:7 / 18
页数:12
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