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Research article - Peer-reviewed, 2021

Spatial prediction and mapping of water quality of Owabi reservoir from satellite imageries and machine learning models

Yeboah Adusei, Yvonne; Quaye-Ballard, Jonathan; Adjaottor, Albert Amatey; Appiah Mensah, Alex

Abstract

Estimation and mapping of surface water quality are vital for the planning and sustainable management of inland reservoirs. The study aimed at retrieving and mapping water quality parameters (WQPs) of Owabi Dam reservoir from Sentinel-2 (S2) and Landsat 8 (L8) satellite data, using random forests (RF), support vector machines (SVM) and multiple linear regression (MLR) models. Water samples from 45 systematic plots were analysed for pH, turbidity, alkalinity, total dissolved solids and dissolved oxygen. The performances of all three models were compared in terms of adjusted coefficient of determination (R2.adj), and the root mean square error (RMSE) using repeated k-fold cross-validation procedure. To determine the status of water quality, pixel-level predictions were used to compute model-assisted estimates of WQPs and compared with reference values from the World Health Organization. Generally, all three models produced more accurate results for S2 compared to L8. On average, the inter-sensor relative efficiency showed that S2 outperformed L8 by 67% in retrieving WQPs of the Owabi Dam reservoir. S2 gave the highest accuracy for RF (R2.adj = 95–99%, RMSE = 0.02–3.03) and least for MLR (R2.adj = 55–91%, RMSE = 0.03–3.14). Compared to RF, SVM showed similar results for S2 but with slightly higher RMSEs (0.03–3.99). The estimated pH (7.06), total dissolved solids (39.19 mg/L) and alkalinity (179.60 mg/L) were within acceptable limits, except for turbidity (33.49 mg/L) which exceeded the reference thresholds. The S2 and RF models are recommended for the monitoring of surface water quality of the Owabi Dam reservoir.

Keywords

Water quality; Optical satellite image data; Machine learning models; Owabi Reservoir

Published in

The Egyptian journal of remote sensing and space sciences
2021, volume: 24, number: 3, pages: 825-833

Authors' information

Yeboah Adusei, Yvonne
Kwame Nkrumah University of Science and Technology (KNUST)
Quaye-Ballard, Jonathan
Kwame Nkrumah University of Science and Technology (KNUST)
Adjaottor, Albert Amatey
Kwame Nkrumah University of Science and Technology (KNUST)
Swedish University of Agricultural Sciences, Department of Forest Resource Management

Sustainable Development Goals

SDG6 Clean water

UKÄ Subject classification

Oceanography, Hydrology, Water Resources

Publication Identifiers

DOI: https://doi.org/10.1016/j.ejrs.2021.06.006

URI (permanent link to this page)

https://res.slu.se/id/publ/112686