A coupled geostatistical and machine learning approach to address spatial prediction of trace metals and pollution indices in sediments of the abandoned gold mining site of Bekao, Adamawa, Cameroon

Reynolds Yvan Abende Sayom; Martin Luther Mfenjou; Mouhamed Ayiwouo Ngounouno; Michele Maguy Cathya Etoundi; William André Boroh; Luc Leroy Mambou Ngueyep; Arsene Meying

Heliyon (Aug 2023)

A coupled geostatistical and machine learning approach to address spatial prediction of trace metals and pollution indices in sediments of the abandoned gold mining site of Bekao, Adamawa, Cameroon

Reynolds Yvan Abende Sayom,
Martin Luther Mfenjou,
Mouhamed Ayiwouo Ngounouno,
Michele Maguy Cathya Etoundi,
William André Boroh,
Luc Leroy Mambou Ngueyep,
Arsene Meying

Affiliations

Reynolds Yvan Abende Sayom: School of Geology and Mining Engineering, University of Ngaoundere, P.O. Box 115, Meiganga, Cameroon; Corresponding author.
Martin Luther Mfenjou: School of Geology and Mining Engineering, University of Ngaoundere, P.O. Box 115, Meiganga, Cameroon
Mouhamed Ayiwouo Ngounouno: School of Geology and Mining Engineering, University of Ngaoundere, P.O. Box 115, Meiganga, Cameroon
Michele Maguy Cathya Etoundi: School of Geology and Mining Engineering, University of Ngaoundere, P.O. Box 115, Meiganga, Cameroon
William André Boroh: School of Geology and Mining Engineering, University of Ngaoundere, P.O. Box 115, Meiganga, Cameroon
Luc Leroy Mambou Ngueyep: School of Geology and Mining Engineering, University of Ngaoundere, P.O. Box 115, Meiganga, Cameroon; Laboratory of Mechanics and Materials of Civil Engineering (L2MGC), CY Cergy Paris University, 5 Mail Gay Lussac, Neuville sur Oise, F-95031, Cergy-Pontoise Cedex, France
Arsene Meying: School of Geology and Mining Engineering, University of Ngaoundere, P.O. Box 115, Meiganga, Cameroon

Journal volume & issue: Vol. 9, no. 8
p. e18511

Abstract

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Trace metals present in high amounts in aquatic systems are a perpetual concern. This study applied geostatistical and machine learning models namely Ordinary Kriging (OK), Ordinary Cokriging (OCK) and Artificial Neural Network (ANN) to assess the spatial variability of trace metals and pollution indices in surface sediments along the Lom River in an abandoned gold mining site at Bekao (Adamawa Cameroon). For this purpose, thirty-one (31) surface sediment samples are collected in order to determine the total concentrations of As, Cr, Cu, Fe, Mn, Ni, Pb, Sn and Zn. These trace metals are used to compute pollution indices as the sediment pollution index (SPI), the Nemerow index (NI), the modified contamination degree (mCD), and the potential ecological risk assessment (RI). OK, OCK and ANN models are compared to determine the best model performance. The best models are selected based on the values of the root mean square error (RMSE), the coefficient of determination (R2), the scatter index (SI) and the BIAS. Results showed that the sequence of trace metal mean concentrations in the sediments is Fe > Mn > Cu > Ni > Sn > Cr > Zn > Pb > As. The mean concentrations of Ni, Cu, Zn and Sn are above the average shale values (ASV) and the pollution status is globally moderate to significant with a low potential ecological risk. The spatial dependency obtained with semivariogram models are moderate to weak for Mn, Fe, Ni, Pb, SPI, NI, mCD, RI As, Cr, and Sn and strong for Cu and Zn. According to cross-validation parameters, ANN model is the best method for the prediction on trace metal concentrations and pollution indices in surface sediments along the Lom River in the abandoned gold mining site of Bekao.

Published in Heliyon

ISSN: 2405-8440 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Science: Science (General); Social Sciences: Social sciences (General)
Website: https://www.cell.com/heliyon/home

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