Szczegóły publikacji
Opis bibliograficzny
Assessing surface water pollution in Hanoi, Vietnam, using remote sensing and machine learning algorithms / Thi-Nhung Do, Diem-My Thi Nguyen, Jiwnath Ghimire, Kim-Chi Vu, Lam-Phuong Do Dang, Sy-Liem Pham, Van-Manh Pham // Environmental Science and Pollution Research ; ISSN 0944-1344 . — 2023 — vol. 30 iss. 34, s. 82230–82247. — Bibliogr. s. 82244–82247, Abstr. — Publikacja dostępna online od: 2023-06-15. — Diem‑My Thi Nguyen – afiliacja: VNU University of Science, Vietnam National University, Hanoi
Autorzy (7)
- Do Thi-Nhung
- Nguyen Thi Diem My
- Ghimire Jiwnath
- Vu Kim-Chi
- Do Dang Lam-Phuong
- Pham Sy-Liem
- Pham Van-Manh
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165935 |
|---|---|
| Data dodania do BaDAP | 2026-03-09 |
| Tekst źródłowy | URL |
| DOI | 10.1007/s11356-023-28127-2 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Environmental Science and Pollution Research |
Abstract
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.
