Advancing non-optical water quality monitoring in Lake Tana, Ethiopia: insights from machine learning and remote sensing techniques

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cg.contributor.affiliationBahir Dar Universityen_US
cg.contributor.affiliationInternational Water Management Instituteen_US
cg.contributor.donorInternational Development Research Centreen_US
cg.contributor.donorSwedish International Development Cooperation Agencyen_US
cg.contributor.donorUnited States Agency for International Developmenten_US
cg.contributor.donorCGIAR Trust Funden_US
cg.contributor.initiativeWest and Central African Food Systems Transformationen_US
cg.coverage.countryEthiopiaen_US
cg.coverage.iso3166-alpha2ETen_US
cg.coverage.regionEastern Africaen_US
cg.coverage.subregionLake Tanaen_US
cg.creator.identifierSeifu Tilahun: 0000-0002-5219-4527en_US
cg.identifier.doihttps://doi.org/10.3389/frwa.2024.1432280en_US
cg.identifier.iwmilibraryH053132en_US
cg.identifier.projectIWMI - C-0018en_US
cg.identifier.projectIWMI - D-0015en_US
cg.isijournalISI Journalen_US
cg.issn2624-9375en_US
cg.journalFrontiers in Wateren_US
cg.reviewStatusPeer Reviewen_US
cg.volume6en_US
dc.contributor.authorLeggesse, E. S.en_US
dc.contributor.authorZimale, F. A.en_US
dc.contributor.authorSultan, D.en_US
dc.contributor.authorEnku, T.en_US
dc.contributor.authorTilahun, Seifu A.en_US
dc.date.accessioned2024-09-30T20:48:54Zen_US
dc.date.available2024-09-30T20:48:54Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/152513en_US
dc.titleAdvancing non-optical water quality monitoring in Lake Tana, Ethiopia: insights from machine learning and remote sensing techniquesen_US
dcterms.abstractWater 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, and 0.81 mg/L, 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.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.available2024-08-20en_US
dcterms.bibliographicCitationLeggesse, E. S.; Zimale, F. A.; Sultan, D.; Enku, T.; Tilahun, Seifu A. 2024. Advancing non-optical water quality monitoring in Lake Tana, Ethiopia: insights from machine learning and remote sensing techniques. Frontiers in Water, 6:1432280. [doi: https://doi.org/10.3389/frwa.2024.1432280]en_US
dcterms.extent1432280en_US
dcterms.issued2024-08-20en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-4.0en_US
dcterms.publisherFrontiers Mediaen_US
dcterms.subjectwater qualityen_US
dcterms.subjectmonitoringen_US
dcterms.subjectinland watersen_US
dcterms.subjectlandsaten_US
dcterms.subjectmachine learningen_US
dcterms.subjectremote sensingen_US
dcterms.subjectsatellite imageryen_US
dcterms.subjecttotal nitrogenen_US
dcterms.subjecttotal phosphorusen_US
dcterms.subjectneural networksen_US
dcterms.typeJournal Articleen_US

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