Assessing surface water pollution in Hanoi, Vietnam, using remote sensing and machine learning algorithms

• Published in: Environmental science and pollution research international Vol. 30; no. 34; pp. 82230 - 82247
• Main Authors: Do, Thi-Nhung, Nguyen, Diem-My Thi, Ghimire, Jiwnath, Vu, Kim-Chi, Do Dang, Lam-Phuong, Pham, Sy-Liem, Pham, Van-Manh
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-07-2023; Springer Nature B.V
• Subjects: Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Biochemical oxygen demand; Chemical oxygen demand; Developing countries; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental Monitoring - methods; Environmental Pollutants; Environmental science; Land use; LDCs; Learning algorithms; Machine Learning; Monitoring methods; Observational learning; Pollutants; Pollution monitoring; Radar; Radar data; Radar imaging; Radar satellites; Regression analysis; Regression models; Remote sensing; Remote Sensing Technology; Research Article; Satellite imagery; Sediments; Surface water; Suspended sediments; Urban planning; Urbanization; Vietnam; Waste Water Technology; Water Management; Water monitoring; Water pollution; Water Pollution Control; Water Quality; Water quality management; Water resources; Vietnam; Surface water pollution; Hanoi City; Remote sensing; Water quality parameters; Machine learning
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-023-28127-2

Rapid urbanization led to significant land-use changes and posed threats to surface water bodies worldwide, especially in the Global South. Hanoi, the capital city of Vietnam, has been facing chronic surface water pollution for more than a decade. Developing a methodology to better track and analyze pollutants using available technologies to manage the problem has been imperative. Advancement of machine learning and earth observation systems offers opportunities for tracking water quality indicators, especially the increasing pollutants in the surface water bodies. This study introduces machine learning with the cubist model (ML-CB), which combines optical and RADAR data, and a machine learning algorithm to estimate surface water pollutants including total suspended sediments (TSS), chemical oxygen demand (COD), and biological oxygen demand (BOD). The model was trained using optical (Sentinel-2A and Sentinel-1A) and RADAR satellite images. Results were compared with field survey data using regression models. Results show that the predictive estimates of pollutants based on ML-CB provide significant results. The study offers an alternative water quality monitoring method for managers and urban planners, which could be instrumental in protecting and sustaining the use of surface water resources in Hanoi and other cities of the Global South.