Evidence map›Paper›PMID 38596013›Full record

ArticleHeliyon2024

Application of machine learning techniques to predict groundwater quality in the Nabogo Basin, Northern Ghana.

Joseph Nzotiyine Apogba, Geophrey Kwame Anornu, Arthur B Koon, Benjamin Wullobayi Dekongmen, Emmanuel Daanoba Sunkari, Obed Fiifi Fynn, Prosper Kpiebaya

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In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Joseph Nzotiyine ApogbaCivil Engineering Department-Regional Water and Environmental Sanitation Centre Kumasi, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Geophrey Kwame AnornuCivil Engineering Department-Regional Water and Environmental Sanitation Centre Kumasi, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Arthur B KoonCivil Engineering Department-Regional Water and Environmental Sanitation Centre Kumasi, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Benjamin Wullobayi DekongmenDepartment of Agricultural Engineering, Ho Technical University, Ho, Ghana.
Emmanuel Daanoba SunkariDepartment of Geology, Faculty of Science, University of Johannesburg, Auckland Park 2006, Kingsway Campus, P. O. Box 524, Johannesburg, South Africa.
Obed Fiifi FynnWater Research Institute - Council for Scientific and Industrial Research, Ghana.
Prosper KpiebayaDepartment of Agricultural Engineering, School of Engineering, University for Development Studies, P. O. Box TL 1882, Ghana.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The main objective of this study was to map the quality of groundwater for domestic use in the Nabogo Basin, a sub-catchment of the White Volta Basin in Ghana, by applying machine learning techniques. The study was conducted by applying the Random Forest (RF) machine learning algorithm to predict groundwater quality, by utilizing factors that influence groundwater occurrence and quality such as Elevation, Topographical Wetness Index (TWI), Slope length (LS), Lithology, Soil type, Normalize Different Vegetation Index (NDVI), Rainfall, Aspect, Slope, Plan Curvature (PLC), Profile Curvature (PRC), Lineament density, Distance to faults, and Drainage density. The groundwater quality of the area was predicted by building a Random Forest model based on computed Arithmetic Water Quality Indices (WQI) (as dependent variable) of existing boreholes, to serve as an indicator of the groundwater quality. The predicted WQI of groundwater in the study area shows that it ranges from 9.51 to 69.99%. This implied that 21.97 %, 74.40 %, and 3.63 % of the study area had respectively the likelihood of excellent. The models were found to perform much better with an RMSE of 23.03 and an R

Indexed as

Groundwater qualityMachine learningNabogo basinWhite Volta Bain

Identifiers

PMID38596013
PMCPMC11002067

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.