watex: machine learning research in water exploration

被引:5
作者
Kouadio, Kouao Laurent [1 ,2 ,3 ]
Liu, Jianxin [1 ,2 ]
Liu, Rong [1 ,2 ]
机构
[1] Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Hunan, Peoples R China
[2] Hunan Key Lab Nonferrous Resources & Geol Hazards, Changsha 410083, Hunan, Peoples R China
[3] Univ Felix Houphouet Boigny, UFR Sci Terre & Ressources Minieres, 22 BP 582 Abidjan 22, Abidjan, Cote Ivoire
基金
中国国家自然科学基金;
关键词
!text type='Python']Python[!/text; machine learning; algorithms; hydro-geophysics; water; UNCERTAINTY QUANTIFICATION;
D O I
10.1016/j.softx.2023.101367
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Water exploration is a scientific domain mostly devoted to the hydro-geophysics field. For instance, geophysical methods such as direct-current, electromagnetic (EM), and logging are primarily used in companionship with pure hydrogeological techniques to propose the right location for drilling operations and determine the permeability coefficient (k) parameter. Unfortunately, despite this combination, unsuccessful, unsustainable boreholes are persisting and the k parameter collection remains difficult and costly thereby creating a huge loss for funders, geophysical and drilling ventures. watex library brings efficient algorithms and smart approaches to solve these issues. Indeed, the recovery of loss EM signals, the automatic location detection for drilling operations, the prediction of flow rate, and the mixture learning strategy using machine learning are some sustainable solutions developed by watex to reduce the numerous losses for future hydro-geophysical engineering projects.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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页数:7
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