Evaluation of empirical and machine learning algorithms for estimation of coastal water quality parameters

Coastal waters are one of the most vulnerable resources that require effective monitoring programs. One of the key factors for effective coastal monitoring is the use of remote sensing technologies that significantly capture the spatiotemporal variability of coastal waters. Optical properties of coastal waters are strongly linked to components, such as colored dissolved organic matter (CDOM), chlorophyll-a (Chl-a), and suspended solids (SS) concentrations, which are essential for the survival of a coastal ecosystem and usually independent of each other. Thus, developing effective remote sensing models to estimate these important water components based on optical properties of coastal waters is mandatory for a successful coastal monitoring program. This study attempted to evaluate the performance of empirical predictive models (EPM) and neural networks (NN)-based algorithms to estimate Chl-a and SS concentrations, in the coastal area of Hong Kong. Remotely-sensed data over a 13-year period was used to develop regional and local models to estimate Chl-a and SS over the entire Hong Kong waters and for each water class within the study area, respectively. The accuracy of regional models derived from EPM and NN in estimating Chl-a and SS was 83%, 93%, 78%, and 97%, respectively, whereas the accuracy of local models in estimating Chl-a and SS ranged from 60–94% and 81–94%, respectively. Both the regional and local NN models exhibited a higher performance than those models derived from empirical analysis. Thus, this study suggests using machine learning methods (i.e., NN) for the more accurate and efficient routine monitoring of coastal water quality parameters (i.e., Chl-a and SS concentrations) over the complex coastal area of Hong Kong and other similar coastal environments.