Inland harmful cyanobacterial bloom prediction in the eutrophic Tri An Reservoir using satellite band ratio and machine learning approaches

• Published in: Environmental science and pollution research international 2020-03, Vol.27 (9), p.9135-9151
• Main Authors: Nguyen, Hao-Quang, Ha, Nam-Thang, Pham, Thanh-Luu
• Format: Article
• Language: English
• Subjects: Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Chlorophyll; Chlorophyll - chemistry; Cyanobacteria; Cyanobacteria - chemistry; Drinking water; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Eutrophic environments; Eutrophic waters; Eutrophication; Gaussian process; Lakes; Learning algorithms; Machine Learning; Mathematical models; Parameters; Public health; Rainy season; Regression analysis; Research Article; Reservoirs; Retrieval; Vietnam; Waste Water Technology; Water Management; Water Pollution Control; Water quality; Waterborne diseases
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-019-07519-3

In recent years, Tri An, a drinking water reservoir for millions of people in southern Vietnam, has been affected by harmful cyanobacterial blooms (HCBs), raising concerns about public health. It is, therefore, crucial to gain insights into the outbreak mechanism of HCBs and understand the spatiotemporal variations of chlorophyll-a (Chl-a) in this highly turbid and productive water. This study aims to evaluate the predictable performance of both approaches using satellite band ratio and machine learning for Chl-a concentration retrieval—a proxy of HCBs. The monthly water quality samples collected from 2016 to 2018 and 23 cloud free Sentinel-2A/B scenes were used to develop Chl-a retrieval models. For the band ratio approach, a strong linear relationship with in situ Chl-a was found for two-band algorithm of Green-NIR. The band ratio-based model accounts for 72% of variation in Chl-a concentration from 2016 to 2018 datasets with an RMSE of 5.95 μg/L. For the machine learning approach, Gaussian process regression (GPR) yielded superior results for Chl-a prediction from water quality parameters with the values of 0.79 ( R 2 ) and 3.06 μg/L (RMSE). Among various climatic parameters, a high correlation ( R 2  = 0.54) between the monthly total precipitation and Chl-a concentration was found. Our analysis also found nitrogen-rich water and TSS in the rainy season as the driving factors of observed HCBs in the eutrophic Tri An Reservoir (TAR), which offer important solutions to the management of HCBs in the future.