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,...

Full description

Saved in:

Bibliographic Details
Main Authors:	Jianlong Xu, Zhuo Xu, Jianjun Kuang, Che Lin, Lianghong Xiao, Xingshan Huang, Yufeng Zhang
Format:	article
Language:	EN
Published:	MDPI AG 2021
Subjects:	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
Online Access:	https://doaj.org/article/748aeb620b3b483e931c6a4bdeeb7d7b
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

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

Similar Items