Machine learning method for quick identification of water quality index (WQI) based on Sentinel-2 MSI data: Ebinur Lake case study
Surface water quality is an important factor affecting the ecological environment and human living environment. The monitoring of surface water quality by remote sensing monitoring technology can provide important research significance for water resources protection and water quality evaluation. Fin...
Enregistré dans:
Auteurs principaux: | Xiaohang Li, Jianli Ding, Nurmemet Ilyas |
---|---|
Format: | article |
Langue: | EN |
Publié: |
IWA Publishing
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/7561cf7bc6854a34a8a0fca968e22c3b |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Water Quality Index (WQI) as a Potential Proxy for Remote Sensing Evaluation of Water Quality in Arid Areas
par: Fei Zhang, et autres
Publié: (2021) -
Remotely observed variations of reservoir low concentration chromophoric dissolved organic matter and its response to upstream hydrological and meteorological conditions using Sentinel-2 imagery and Gradient Boosting Regression Tree
par: Zeliang Zhang, et autres
Publié: (2021) -
Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia
par: Taimi S. Kapalanga, et autres
Publié: (2021) -
Machine learning techniques in river water quality modelling: a research travelogue
par: Sakshi Khullar, et autres
Publié: (2021) -
Evaluation of surface water quality using water quality indices (WQIs) in Lake Sukhna, Chandigarh, India
par: Maansi, et autres
Publié: (2021)