Evaluation of machine learning methods for forecasting turbidity in river networks using Sentinel-2 remote sensing data
Turbidity is an important indicator of river water quality and of great interest to improve the data acquisition methods and efficiency of decision support systems for sustainable ecosystem management. However, river water quality monitoring stations are very expensive to operate and maintain and la...
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S157495412500322X |
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| author | Victor Oliveira Santos Paulo Alexandre Costa Rocha Jesse Van Griensven Thé Bahram Gharabaghi |
| author_facet | Victor Oliveira Santos Paulo Alexandre Costa Rocha Jesse Van Griensven Thé Bahram Gharabaghi |
| author_sort | Victor Oliveira Santos |
| collection | DOAJ |
| description | Turbidity is an important indicator of river water quality and of great interest to improve the data acquisition methods and efficiency of decision support systems for sustainable ecosystem management. However, river water quality monitoring stations are very expensive to operate and maintain and lack spatial coverage. Therefore, this study takes advantage of the vast spatial coverage of remote sensing datasets from satellites to provide a more efficient hybrid system with comprehensive coverage of both spatial and temporal changes in water quality across a vast river network. Spectral bands from Sentinel-2 were analyzed using machine learning algorithms, namely XGBoost, Random Forests, GMDH, Support Vector Regression, k-Nearest Neighbors and Least Absolute Shrinkage and Selection Operator to model turbidity, using data from twelve monitoring stations across the Mississippi River, USA. Results show that considering the individual monitoring stations, the ML algorithms for turbidity modeling were satisfactory at locations with a larger range and standard deviation of turbidity values, achieving a mean R2 value of 59.5 %. Tree-based models were the best overall approach, often ranking as the best or second-best performing model. Using all the samples from the monitoring stations, the XGBoost provided a superior output for turbidity modeling, reaching R2 equal to 75.7 %. This represents an improvement of over 16 % compared to the average metric value for the individual stations. A comprehensive comparison with the literature found that the models implemented using this study's methodology could provide competitive results, deeming it as an alternative for turbidity modeling from remote sensing data. |
| format | Article |
| id | doaj-art-9018d15261cc40a29b16d4d9b2a8764e |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-9018d15261cc40a29b16d4d9b2a8764e2025-08-20T05:05:43ZengElsevierEcological Informatics1574-95412025-12-019010331310.1016/j.ecoinf.2025.103313Evaluation of machine learning methods for forecasting turbidity in river networks using Sentinel-2 remote sensing dataVictor Oliveira Santos0Paulo Alexandre Costa Rocha1Jesse Van Griensven Thé2Bahram Gharabaghi3School of Engineering, University of Guelph, 50 Stone Rd E, Guelph, ON N1G 2W1, CanadaSchool of Engineering, University of Guelph, 50 Stone Rd E, Guelph, ON N1G 2W1, Canada; Mechanical Engineering Department, Technology Center, Federal University of Ceará, Fortaleza, Ceará 60020-181, BrazilSchool of Engineering, University of Guelph, 50 Stone Rd E, Guelph, ON N1G 2W1, Canada; Lakes Environmental, 170 Columbia St. W, Waterloo, ON N2L 3L3, CanadaSchool of Engineering, University of Guelph, 50 Stone Rd E, Guelph, ON N1G 2W1, Canada; Corresponding author.Turbidity is an important indicator of river water quality and of great interest to improve the data acquisition methods and efficiency of decision support systems for sustainable ecosystem management. However, river water quality monitoring stations are very expensive to operate and maintain and lack spatial coverage. Therefore, this study takes advantage of the vast spatial coverage of remote sensing datasets from satellites to provide a more efficient hybrid system with comprehensive coverage of both spatial and temporal changes in water quality across a vast river network. Spectral bands from Sentinel-2 were analyzed using machine learning algorithms, namely XGBoost, Random Forests, GMDH, Support Vector Regression, k-Nearest Neighbors and Least Absolute Shrinkage and Selection Operator to model turbidity, using data from twelve monitoring stations across the Mississippi River, USA. Results show that considering the individual monitoring stations, the ML algorithms for turbidity modeling were satisfactory at locations with a larger range and standard deviation of turbidity values, achieving a mean R2 value of 59.5 %. Tree-based models were the best overall approach, often ranking as the best or second-best performing model. Using all the samples from the monitoring stations, the XGBoost provided a superior output for turbidity modeling, reaching R2 equal to 75.7 %. This represents an improvement of over 16 % compared to the average metric value for the individual stations. A comprehensive comparison with the literature found that the models implemented using this study's methodology could provide competitive results, deeming it as an alternative for turbidity modeling from remote sensing data.http://www.sciencedirect.com/science/article/pii/S157495412500322XSentinel satellite constellationMachine learningTurbiditySpectral indicesMississippi riverMissouri river |
| spellingShingle | Victor Oliveira Santos Paulo Alexandre Costa Rocha Jesse Van Griensven Thé Bahram Gharabaghi Evaluation of machine learning methods for forecasting turbidity in river networks using Sentinel-2 remote sensing data Ecological Informatics Sentinel satellite constellation Machine learning Turbidity Spectral indices Mississippi river Missouri river |
| title | Evaluation of machine learning methods for forecasting turbidity in river networks using Sentinel-2 remote sensing data |
| title_full | Evaluation of machine learning methods for forecasting turbidity in river networks using Sentinel-2 remote sensing data |
| title_fullStr | Evaluation of machine learning methods for forecasting turbidity in river networks using Sentinel-2 remote sensing data |
| title_full_unstemmed | Evaluation of machine learning methods for forecasting turbidity in river networks using Sentinel-2 remote sensing data |
| title_short | Evaluation of machine learning methods for forecasting turbidity in river networks using Sentinel-2 remote sensing data |
| title_sort | evaluation of machine learning methods for forecasting turbidity in river networks using sentinel 2 remote sensing data |
| topic | Sentinel satellite constellation Machine learning Turbidity Spectral indices Mississippi river Missouri river |
| url | http://www.sciencedirect.com/science/article/pii/S157495412500322X |
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