remote sensing Article Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong Sidrah Hafeez 1 , Man Sing Wong 1,* , Hung Chak Ho 2 , Majid Nazeer 3,4 , Janet Nichol 5 , Sawaid Abbas 1, Danling Tang 6, Kwon Ho Lee 7 and Lilian Pun 1 1 Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong;
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[email protected] 4 Earth & Atmospheric Remote Sensing Lab (EARL), Department of Meteorology, COMSATS University Islamabad, Islamabad 45550, Pakistan 5 Department of Geography, University of Sussex, Brighton BN1 9RH, UK;
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[email protected] 7 Department of Atmospheric & Environmental Sciences, Gangneung–Wonju National University, Gangneung, Gangwondo 25457, Korea;
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[email protected]; Tel.: +852-3400-8959 Received: 15 February 2019; Accepted: 7 March 2019; Published: 13 March 2019 Abstract: 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.