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

Yeboah Adusei, Yvonne and Quaye-Ballard, Jonathan and Adjaottor, Albert Amatey and Appiah Mensah, Alex (2021). Spatial prediction and mapping of water quality of Owabi reservoir from satellite imageries and machine learning models. The Egyptian journal of remote sensing and space sciences. 24 :3 , 825-833
[Research article]

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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.

Authors/Creators:Yeboah Adusei, Yvonne and Quaye-Ballard, Jonathan and Adjaottor, Albert Amatey and Appiah Mensah, Alex
Title:Spatial prediction and mapping of water quality of Owabi reservoir from satellite imageries and machine learning models
Series Name/Journal:The Egyptian journal of remote sensing and space sciences
Year of publishing :2021
Volume:24
Number:3
Page range:825-833
Number of Pages:9
ISSN:1110-9823
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 105 Earth and Related Environmental Sciences > Oceanography, Hydrology, Water Resources
Keywords:Water quality, Optical satellite image data, Machine learning models, Owabi Reservoir
URN:NBN:urn:nbn:se:slu:epsilon-p-112686
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-112686
Additional ID:
Type of IDID
DOI10.1016/j.ejrs.2021.06.006
Web of Science (WoS)000783904700003
Scopus2-s2.0-85108960221
ID Code:28174
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:01 Jun 2022 14:26
Metadata Last Modified:01 Jun 2022 14:31

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