An Alternative to Laboratory Testing: Random Forest-Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies

An Alternative to Laboratory Testing: Random Forest-Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies

Water quality monitoring plays a vital role in the water environment management, while efficient monitoring provides direction and verification of the effectiveness of water management. Traditional water quality monitoring for a variety of water parameters requires the placement of multiple sensors,...

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Autores principales: Jianlong Xu, Zhuo Xu, Jianjun Kuang, Che Lin, Lianghong Xiao, Xingshan Huang, Yufeng Zhang
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
water quality prediction
machine learning
total nitrogen
random forest
google earth engine
Hydraulic engineering
TC1-978
Water supply for domestic and industrial purposes
TD201-500
Acceso en línea:https://doaj.org/article/748aeb620b3b483e931c6a4bdeeb7d7b
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https://doaj.org/article/748aeb620b3b483e931c6a4bdeeb7d7b

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