Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong
Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-03-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/11/6/617 |
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| author | Sidrah Hafeez Man Sing Wong Hung Chak Ho Majid Nazeer Janet Nichol Sawaid Abbas Danling Tang Kwon Ho Lee Lilian Pun |
| author_facet | Sidrah Hafeez Man Sing Wong Hung Chak Ho Majid Nazeer Janet Nichol Sawaid Abbas Danling Tang Kwon Ho Lee Lilian Pun |
| author_sort | Sidrah Hafeez |
| collection | DOAJ |
| description | Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters. |
| first_indexed | 2024-12-24T04:18:39Z |
| format | Article |
| id | doaj.art-c85b233141c6461b9991e90f0e812edd |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T04:18:39Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c85b233141c6461b9991e90f0e812edd2022-12-21T17:15:52ZengMDPI AGRemote Sensing2072-42922019-03-0111661710.3390/rs11060617rs11060617Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong KongSidrah Hafeez0Man Sing Wong1Hung Chak Ho2Majid Nazeer3Janet Nichol4Sawaid Abbas5Danling Tang6Kwon Ho Lee7Lilian Pun8Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Kowloon, Hong KongDepartment of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Kowloon, Hong KongDepartment of Urban Planning and Design, The University of Hong Kong, Hong KongKey Laboratory of Digital Land and resources, East China University of Technology, Nanchang 330013, ChinaDepartment of Geography, University of Sussex, BN1 9RH Brighton, UKDepartment of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Kowloon, Hong KongSouth China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, ChinaDepartment of Atmospheric & Environmental Sciences, Gangneung–Wonju National University, Gangneung, Gangwondo 25457, KoreaDepartment of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Kowloon, Hong KongAnthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters.http://www.mdpi.com/2072-4292/11/6/617Chlorophyll-aturbiditysuspended solidsmachine learningLandsat |
| spellingShingle | Sidrah Hafeez Man Sing Wong Hung Chak Ho Majid Nazeer Janet Nichol Sawaid Abbas Danling Tang Kwon Ho Lee Lilian Pun Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong Remote Sensing Chlorophyll-a turbidity suspended solids machine learning Landsat |
| title | Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong |
| title_full | Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong |
| title_fullStr | Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong |
| title_full_unstemmed | Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong |
| title_short | Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong |
| title_sort | comparison of machine learning algorithms for retrieval of water quality indicators in case ii waters a case study of hong kong |
| topic | Chlorophyll-a turbidity suspended solids machine learning Landsat |
| url | http://www.mdpi.com/2072-4292/11/6/617 |
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