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ORIGINAL RESEARCH article

Front. Water
Sec. Water and Artificial Intelligence
Volume 6 - 2024 | doi: 10.3389/frwa.2024.1432280
This article is part of the Research Topic Artificial Intelligence Applications to Water Quality Modeling View all articles

Advancing Non-Optical Water Quality Monitoring in Lake Tana, Ethiopia: Insights from Machine Learning and Remote Sensing Techniques

Provisionally accepted
Elias Leggesse Elias Leggesse 1*Fasikaw A. Zimale Fasikaw A. Zimale 1Dagnenet Sultan Dagnenet Sultan 1Temesgen Enku Temesgen Enku 1Seifu A. Tilahun Seifu A. Tilahun 2*
  • 1 Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, Amhara Region, Ethiopia
  • 2 International Water Management Institute (IWMI), Accra, Ghana

The final, formatted version of the article will be published soon.

    Water quality is deteriorating in the world's freshwater bodies, and Lake Tana in Ethiopia is becoming unpleasant to biodiversity. The objective of this study is to retrieve non-optical water quality data, specifically total nitrogen (TN) and total phosphorus (TP) concentrations, in Lake Tana using Machine Learning (ML) techniques applied to Landsat 8 OLI imagery. The ML methods employed include artificial neural networks (ANN), Support Vector Regression (SVR), Random Forest Regression (RF), XGBoost Regression (XGB), AdaBoost Regression (AB), and Gradient Boosting Regression (GB). The XGB algorithm provided the best result for TN retrieval, with determination coefficient (R²), mean absolute error (MARE), relative mean square error (RMSE) and Nash Sutcliff (NS) values of 0.80, 0.043, 0.52 mg/L, and 0.81, respectively. The RF algorithm was most effective for TP retrieval, with R² of 0.73, MARE of 0.076, RMSE of 0.17 mg/L, and NS index of 0.74. These methods accurately predicted TN and TP spatial concentrations, identifying hotspots along river inlets and northeasters. The temporal patterns of TN, TP, and their ratios were also accurately represented by combining in-situ, RS and ML-based models. Our findings suggest that this approach can significantly improve the accuracy of water quality retrieval in large inland lakes and lead to the development of potential water quality digital services.

    Keywords: Inland waterbodies, Lake Tana, Landsat, machine learning, Non-optical, Water Quality

    Received: 13 May 2024; Accepted: 31 Jul 2024.

    Copyright: © 2024 Leggesse, Zimale, Sultan, Enku and Tilahun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Elias Leggesse, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, Amhara Region, Ethiopia
    Seifu A. Tilahun, International Water Management Institute (IWMI), Accra, Ghana

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