Groundwater flow rate prediction from geo-electrical features using Support Vector Machines: Bagoue dataset, Cote d'Ivoire

Authors: Kouadio, Kouao Laurent; Kouame, Loukou Nicolas; Mi Binbin; Drissa Coulibaly; Gnoleba Serge Pacôme Déguine; Kouamelan Kouamelan Serge; Zhang Hongyu; +1 Authors

doi: 10.5281/zenodo.4896783 , 10.5281/zenodo.4898807 , 10.5281/zenodo.4896713 , 10.5281/zenodo.4897072

Groundwater flow rate prediction from geo-electrical features using Support Vector Machines: Bagoue dataset, Cote d'Ivoire

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Abstract

We implemented a support vector machines to predict groundwater flow before drilling using electrical resistivity profiling, vertical electrical sounding. We used the data from Cote d’Ivoire, west part of Africa and we tested the composite estimator in region of Bagoue, north part of CIV. Data are composed on electrical resistivity profile, vertical electrical sounding and borehole data. The coordinates of different boreholes are also mentioned.

Related Organizations

Zhejiang Ocean University
China (People's Republic of)
Université Félix Houphouët-Boigny
Cote d'Ivoire
China University of Geosciences
China (People's Republic of)

Keywords

Support Vector Machine, Machine learning, borehole, groundwater flow, electrical method

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