ArticleEnvironmental science and pollution research international2020
Inland harmful cyanobacterial bloom prediction in the eutrophic Tri An Reservoir using satellite band ratio and machine learning approaches.
Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Phytoplankton characterization in a tropical tidal river impacted by a megacity: the case of the Saigon River (Southern Vietnam).Environmental science and pollution research international · 2022Article
- Toxic cyanobacteria and microcystin dynamics in a tropical reservoir: assessing the influence of environmental variables.Environmental science and pollution research international · 2021Article
- Status of water use and potential of rainwater harvesting for replacing centralized supply system in remote mountainous areas: a case study.Environmental science and pollution research international · 2021Article
- Health risk assessment related to cyanotoxins exposure of a community living near Tri An Reservoir, Vietnam.Environmental science and pollution research international · 2021Article
- A novel two-step adaptive multioutput semisupervised soft sensor with applications in wastewater treatment.Environmental science and pollution research international · 2021Article
- Estimation of nitrogen and phosphorus concentrations from water quality surrogates using machine learning in the Tri An Reservoir, Vietnam.Environmental monitoring and assessment · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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
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