Research Article
Dissolved Oxygen Concentrations Modeling of the Tighen River Water Using Physicochemical Variables and Various Machine-Learning Algorithms in Guinea Republic
Abdoulaye Missira Bangoura
,
Noukpo Medard Agbazo*,
Saa Moussa Kamano,
Mafory Bangoura,
Kande Bangoura
Issue:
Volume 14, Issue 3, June 2026
Pages:
42-51
Received:
1 May 2026
Accepted:
16 May 2026
Published:
27 May 2026
DOI:
10.11648/j.ajac.20261403.11
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Views:
Abstract: Dissolved oxygen is an essential indicator of water pollution and the critical water quality constituent that impacts aquatic life. Thus, accurate modeling of its concentration is vital for freshwater resource management and protection. Despite this, in the African context, more specifically West Africa, there is virtually no scientific work that has focused on modeling dissolved oxygen concentrations in rivers and lakes. This preliminary work attempted to model and estimate, using others microbiological and physicochemical parameters and machine learning algorithms, the dissolved oxygen concentration of the Tighen River water in the Republic of Guinea. Based on two alternatives, three algorithms such as multiple linear regression (MLR), random forest (RF), and gradient boosting (GB) were employed to model and estimate dissolved oxygen concentrations. Alternative 1 referred to when microbiological and physicochemical parameters exhibiting correlations greater than + 0.1 or less than − 0.1 with dissolved oxygen are used for modeling its concentration, while alternative 2 referred to when variables exhibiting statistically significant correlations with dissolved oxygen are used. Results obtained from the models were evaluated using Nash-Sutcliffe efficiency coefficient (NSE), mean absolute error (MAE), Pearson correlation coefficient (RP), and root mean square error (RMSE) to identify the appropriate alternative and algorithm to model and estimate the dissolved oxygen. In the testing phase, the results showed that (1) among tested alternatives, alternative 2 quasi-systematically presents a smaller RMSE and MAE, and higher NSE and RP, indicating that it is significantly better than the alternative 1. (2) among the employed algorithms, under alternative 2, the RF algorithm exhibits the best performance in modeling dissolved oxygen, therefore, RF outperforms, MLR, and GB algorithm. These findings provide a scientific reference to enhance freshwater resource management and protection in Tighen river.
Abstract: Dissolved oxygen is an essential indicator of water pollution and the critical water quality constituent that impacts aquatic life. Thus, accurate modeling of its concentration is vital for freshwater resource management and protection. Despite this, in the African context, more specifically West Africa, there is virtually no scientific work that h...
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