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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Bibliographic Details
Main Authors: Jianlong Xu, Zhuo Xu, Jianjun Kuang, Che Lin, Lianghong Xiao, Xingshan Huang, Yufeng Zhang
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:Water
Subjects:
water quality prediction
machine learning
total nitrogen
random forest
google earth engine
Online Access:https://www.mdpi.com/2073-4441/13/22/3262

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