Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

被引:131
|
作者
Hafeez, Sidrah [1 ]
Wong, Man Sing [1 ]
Ho, Hung Chak [2 ]
Nazeer, Majid [3 ,4 ]
Nichol, Janet [5 ]
Abbas, Sawaid [1 ]
Tang, Danling [6 ]
Lee, Kwon Ho [7 ]
Pun, Lilian [1 ]
机构
[1] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Kowloon, Hong Kong, Peoples R China
[2] Univ Hong Kong, Dept Urban Planning & Design, Hong Kong, Peoples R China
[3] East China Univ Technol, Key Lab Digital Land & Resources, Nanchang 330013, Jiangxi, Peoples R China
[4] COMSATS Univ Islamabad, Dept Meteorol, EARL, Islamabad 45550, Pakistan
[5] Univ Sussex, Dept Geog, Brighton BN1 9RH, E Sussex, England
[6] Chinese Acad Sci, South China Sea Inst Oceanol, Guangzhou 510301, Guangdong, Peoples R China
[7] Gangneung Wonju Natl Univ, Dept Atmospher & Environm Sci, Kangnung 25457, Gangwondo, South Korea
关键词
Chlorophyll-a; turbidity; suspended solids; machine learning; Landsat; CHLOROPHYLL-A CONCENTRATION; SUPPORT VECTOR MACHINES; PEARL RIVER ESTUARY; SUSPENDED-SOLIDS; COASTAL WATERS; ALGAL BLOOMS; CLASSIFICATION; EUTROPHICATION; TEMPERATE; IMAGERY;
D O I
10.3390/rs11060617
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 mu g/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 mu g/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 approximate to 0.665 mu m) and the product of red and green band (wavelength approximate to 0.560 mu m) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength approximate to 0.490 mu m) as well as the ratio between infrared (wavelength approximate to 0.865 mu m) 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.
引用
收藏
页数:23
相关论文
共 50 条
  • [1] Prediction of water turbidity in a marine environment using machine learning: A case study of Hong Kong
    Kumar, Lalit
    Afzal, Mohammad Saud
    Ahmad, Ashad
    [J]. REGIONAL STUDIES IN MARINE SCIENCE, 2022, 52
  • [2] Personal and Neighbourhood Indicators of Quality of Urban Life: A Case Study of Hong Kong
    Low, Chien-Tat
    Stimson, Robert
    Chen, Si
    Cerin, Ester
    Wong, Paulina Pui-Yun
    Lai, Poh-Chin
    [J]. SOCIAL INDICATORS RESEARCH, 2018, 136 (02) : 751 - 773
  • [3] Personal and Neighbourhood Indicators of Quality of Urban Life: A Case Study of Hong Kong
    Chien-Tat Low
    Robert Stimson
    Si Chen
    Ester Cerin
    Paulina Pui-Yun Wong
    Poh-Chin Lai
    [J]. Social Indicators Research, 2018, 136 : 751 - 773
  • [4] RETRIEVAL OF CASE 2 WATER QUALITY PARAMETERS WITH MACHINE LEARNING
    Ruescas, Ana B.
    Mateo-Garcia, Gonzalo
    Camps-Valls, Gustau
    Hieronymi, Martin
    [J]. IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 124 - 127
  • [5] Integrated water quality management in Tolo Harbour, Hong Kong: a case study
    Chau, K. W.
    [J]. JOURNAL OF CLEANER PRODUCTION, 2007, 15 (16) : 1568 - 1572
  • [6] Spatially adaptive machine learning models for predicting water quality in Hong Kong
    Wang, Qiaoli
    Li, Zijun
    Cai, Jiannan
    Zhang, Mengsheng
    Liu, Zida
    Xu, Yu
    Li, Rongrong
    [J]. JOURNAL OF HYDROLOGY, 2023, 622
  • [7] Academic Staff Views of Quality Systems for Teaching and Learning: a Hong Kong case study
    Jones, John
    De Saram, Don
    [J]. QUALITY IN HIGHER EDUCATION, 2005, 11 (01) : 47 - 58
  • [8] A case study of teacher learning in an assessment for learning project in Hong Kong
    Tang, Sylvia Yee Fan
    Leung, Pamela Pui Wan
    Chow, Alice Wai Kwan
    Wong, Ping Man
    [J]. PROFESSIONAL DEVELOPMENT IN EDUCATION, 2010, 36 (04) : 621 - 636
  • [9] Travel Time Prediction: Comparison of Machine Learning Algorithms in a Case Study
    Goudarzi, Forough
    [J]. IEEE 20TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS / IEEE 16TH INTERNATIONAL CONFERENCE ON SMART CITY / IEEE 4TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND SYSTEMS (HPCC/SMARTCITY/DSS), 2018, : 1404 - 1407
  • [10] Time-Series Prediction of Intense Wind Shear Using Machine Learning Algorithms: A Case Study of Hong Kong International Airport
    Khattak, Afaq
    Chan, Pak-Wai
    Chen, Feng
    Peng, Haorong
    [J]. ATMOSPHERE, 2023, 14 (02)