Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia
Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard field meas...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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MDPI
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/130750 |
| _version_ | 1855536747431067648 |
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| author | Leggesse, E.S. Zimale, Fasikaw A. Sultan, D. Enku, T. Srinivasan, R. Tilahun, Seifu A. |
| author_browse | Enku, T. Leggesse, E.S. Srinivasan, R. Sultan, D. Tilahun, Seifu A. Zimale, Fasikaw A. |
| author_facet | Leggesse, E.S. Zimale, Fasikaw A. Sultan, D. Enku, T. Srinivasan, R. Tilahun, Seifu A. |
| author_sort | Leggesse, E.S. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard field measuring for monitoring water quality in large and remote areas constrained by logistics and finance. Six machine learning (ML) algorithms integrated with Landsat 8 imagery were evaluated for their accuracy in predicting three optically active water quality indicators observed monthly in the period from August 2016 to April 2022: turbidity (TUR), total dissolved solids (TDS) and Chlorophyll a (Chl-a). The six ML algorithms studied were the artificial neural network (ANN), support vector machine regression (SVM), random forest regression (RF), XGBoost regression (XGB), AdaBoost regression (AB), and gradient boosting regression (GB) algorithms. XGB performed best at predicting Chl-a, with an R2 of 0.78, Nash–Sutcliffe efficiency (NSE) of 0.78, mean absolute relative error (MARE) of 0.082 and root mean squared error (RMSE) of 9.79 µg/L. RF performed best at predicting TDS (with an R2 of 0.79, NSE of 0.80, MARE of 0.082, and RMSE of 12.30 mg/L) and TUR (with an R2 of 0.80, NSE of 0.81, and MARE of 0.072 and RMSE of 7.82 NTU). The main challenges were data size, sampling frequency, and sampling resolution. To overcome the data limitation, we used a K-fold cross validation technique that could obtain the most out of the limited data to build a robust model. Furthermore, we also employed stratified sampling techniques to improve the ML modeling for turbidity. Thus, this study shows the possibility of monitoring water quality in large freshwater bodies with limited observed data using remote sensing integrated with ML algorithms, potentially enhancing decision making. |
| format | Journal Article |
| id | CGSpace130750 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1307502025-12-08T10:29:22Z Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia Leggesse, E.S. Zimale, Fasikaw A. Sultan, D. Enku, T. Srinivasan, R. Tilahun, Seifu A. water quality indicators prediction remote sensing machine learning algorithms neural networks modelling total dissolved solids turbidity chlorophyll a landsat satellite imagery monitoring highlands lakes Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard field measuring for monitoring water quality in large and remote areas constrained by logistics and finance. Six machine learning (ML) algorithms integrated with Landsat 8 imagery were evaluated for their accuracy in predicting three optically active water quality indicators observed monthly in the period from August 2016 to April 2022: turbidity (TUR), total dissolved solids (TDS) and Chlorophyll a (Chl-a). The six ML algorithms studied were the artificial neural network (ANN), support vector machine regression (SVM), random forest regression (RF), XGBoost regression (XGB), AdaBoost regression (AB), and gradient boosting regression (GB) algorithms. XGB performed best at predicting Chl-a, with an R2 of 0.78, Nash–Sutcliffe efficiency (NSE) of 0.78, mean absolute relative error (MARE) of 0.082 and root mean squared error (RMSE) of 9.79 µg/L. RF performed best at predicting TDS (with an R2 of 0.79, NSE of 0.80, MARE of 0.082, and RMSE of 12.30 mg/L) and TUR (with an R2 of 0.80, NSE of 0.81, and MARE of 0.072 and RMSE of 7.82 NTU). The main challenges were data size, sampling frequency, and sampling resolution. To overcome the data limitation, we used a K-fold cross validation technique that could obtain the most out of the limited data to build a robust model. Furthermore, we also employed stratified sampling techniques to improve the ML modeling for turbidity. Thus, this study shows the possibility of monitoring water quality in large freshwater bodies with limited observed data using remote sensing integrated with ML algorithms, potentially enhancing decision making. 2023-05-11 2023-06-19T05:29:47Z 2023-06-19T05:29:47Z Journal Article https://hdl.handle.net/10568/130750 en Open Access MDPI Leggesse, E. S.; Zimale, F. A.; Sultan, D.; Enku, T.; Srinivasan, R.; Tilahun, Seifu A. 2023. Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia. Hydrology, 10(5):110. [doi: https://doi.org/10.3390/hydrology10050110] |
| spellingShingle | water quality indicators prediction remote sensing machine learning algorithms neural networks modelling total dissolved solids turbidity chlorophyll a landsat satellite imagery monitoring highlands lakes Leggesse, E.S. Zimale, Fasikaw A. Sultan, D. Enku, T. Srinivasan, R. Tilahun, Seifu A. Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia |
| title | Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia |
| title_full | Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia |
| title_fullStr | Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia |
| title_full_unstemmed | Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia |
| title_short | Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia |
| title_sort | predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of ethiopia |
| topic | water quality indicators prediction remote sensing machine learning algorithms neural networks modelling total dissolved solids turbidity chlorophyll a landsat satellite imagery monitoring highlands lakes |
| url | https://hdl.handle.net/10568/130750 |
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