Next Article in Journal
Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics
Next Article in Special Issue
A Data-Driven Approach to Classifying Wave Breaking in Infrared Imagery
Previous Article in Journal
Processing Chain for Estimation of Tree Diameter from GNSS-IMU-Based Mobile Laser Scanning Data
Previous Article in Special Issue
Ship Classification Based on Multifeature Ensemble with Convolutional Neural Network
Open AccessArticle

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

1
Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
2
Department of Urban Planning and Design, The University of Hong Kong, Hong Kong
3
Key Laboratory of Digital Land and resources, East China University of Technology, Nanchang 330013, China
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
6
South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China
7
Department of Atmospheric & Environmental Sciences, Gangneung–Wonju National University, Gangneung, Gangwondo 25457, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(6), 617; https://doi.org/10.3390/rs11060617
Received: 15 February 2019 / Accepted: 7 March 2019 / Published: 13 March 2019
(This article belongs to the Special Issue AI-based Remote Sensing Oceanography)
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. View Full-Text
Keywords: Chlorophyll-a; turbidity; suspended solids; machine learning; Landsat Chlorophyll-a; turbidity; suspended solids; machine learning; Landsat
Show Figures

Figure 1

MDPI and ACS Style

Hafeez, S.; Wong, M.S.; Ho, H.C.; Nazeer, M.; Nichol, J.; Abbas, S.; Tang, D.; Lee, K.H.; Pun, L. Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong. Remote Sens. 2019, 11, 617.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop