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International Journal of Innovation and Applied Studies
ISSN: 2028-9324     CODEN: IJIABO     OCLC Number: 828807274     ZDB-ID: 2703985-7
 
 
Monday 30 March 2020

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Delineating groundwater potential zones in Western Cameroon Highlands using GIS based Artificial Neural Networks model and remote sensing data


Volume 15, Issue 4, May 2016, Pages 747–759

 Delineating groundwater potential zones in Western Cameroon Highlands using GIS based Artificial Neural Networks model and remote sensing data

Valère - Carin JOFACK SOKENG1, F.K. Kouamé2, Benjamin NGOUNOU NGATCHA3, Hyppolite Dibi N'DA4, Lucette YOU AKPA5, and Dieudonné RIRABE6

1 Centre Universitaire de Recherche et d’Application en Télédétection (CURAT), University Félix Houphouët Boigny of Cocody, Abidjan, Côte d'Ivoire
2 Centre Universitaire de Recherche et d’Application en Télédétection (CURAT), University Félix Houphouët Boigny of Cocody, Abidjan, Côte d'Ivoire
3 Department of Earth Sciences, University of Ngaoundere, Ngaoundere, Cameroon
4 Centre Universitaire de Recherche et d’Application en Télédétection (CURAT), University Félix Houphouët Boigny of Cocody, Abidjan, Côte d'Ivoire
5 Centre Universitaire de Recherche et d’Application en Télédétection (CURAT), University Félix Houphouët Boigny of Cocody, Abidjan, Côte d'Ivoire
6 Institut Polytechnique de Moungo, Moungo, Tchad

Original language: English

Received 27 February 2016

Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


For the sustainable use of groundwater, this study analyzes groundwater potential in Western Cameroon Highlands using artificial neural network model (ANN), GIS tools and remote sensing. Twelve factors believed to influence the groundwater occurrence were selected from literature and field investigations and used as input data. Satellite ALOS PALSAR, LANDSAT OLI, SRTM data processing techniques and GIS spatial analysis tools were used to prepare these maps. Pumping rates from 189 wells were considered as groundwater potential data and randomly divided into a training and a test sets. An ANN based on the relationship between groundwater productivity data and the above factors was implement on MATLAB. Each factor

Author Keywords: groundwater potential, remote sensing, GIS, artificial neural networks, Western Cameroon Highlands.


How to Cite this Article


Valère - Carin JOFACK SOKENG, F.K. Kouamé, Benjamin NGOUNOU NGATCHA, Hyppolite Dibi N'DA, Lucette YOU AKPA, and Dieudonné RIRABE, “Delineating groundwater potential zones in Western Cameroon Highlands using GIS based Artificial Neural Networks model and remote sensing data,” International Journal of Innovation and Applied Studies, vol. 15, no. 4, pp. 747–759, May 2016.