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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spelling	oai:doaj.org-article:748aeb620b3b483e931c6a4bdeeb7d7b2021-11-25T19:16:11ZAn Alternative to Laboratory Testing: Random Forest-Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies10.3390/w132232622073-4441https://doaj.org/article/748aeb620b3b483e931c6a4bdeeb7d7b2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4441/13/22/3262https://doaj.org/toc/2073-4441Water 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, and some water quality data (e.g., total nitrogen (TN)) requires testing instruments or laboratory analysis to obtain results, which takes longer than the sensors. In this paper, we designed a water quality prediction framework, which uses available water quality variables (e.g., temperature, pH, conductivity, etc.) to predict total nitrogen concentrations in inland water bodies. The framework was also used to predict nearshore seawater salinity and temperature using remote sensing bands. We conducted experiments on real water quality datasets and random forest was chosen to be the core algorithm of the framework by comparing and analyzing the performance of different machine learning algorithms. The results show that among all tested machine learning models, random forest performs the best. The data prediction error rate of the random forest model in predicting the total nitrogen concentration in inland rivers was 4.9%. Moreover, to explore the prediction effect of random forest algorithm when the independent variable is non-water quality data, we took the reflectance of remote sensing bands as the independent variables and successfully inverted the salinity distribution of Shenzhen Bay in the Google Earth Engine (GEE) platform. According to the experimental results, the random forest-based water quality prediction framework can achieve 92.94% accuracy in predicting the salinity of nearshore waters.Jianlong XuZhuo XuJianjun KuangChe LinLianghong XiaoXingshan HuangYufeng ZhangMDPI AGarticlewater quality predictionmachine learningtotal nitrogenrandom forestgoogle earth engineHydraulic engineeringTC1-978Water supply for domestic and industrial purposesTD201-500ENWater, Vol 13, Iss 3262, p 3262 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	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
spellingShingle	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 Jianlong Xu Zhuo Xu Jianjun Kuang Che Lin Lianghong Xiao Xingshan Huang Yufeng Zhang An Alternative to Laboratory Testing: Random Forest-Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies
description	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, and some water quality data (e.g., total nitrogen (TN)) requires testing instruments or laboratory analysis to obtain results, which takes longer than the sensors. In this paper, we designed a water quality prediction framework, which uses available water quality variables (e.g., temperature, pH, conductivity, etc.) to predict total nitrogen concentrations in inland water bodies. The framework was also used to predict nearshore seawater salinity and temperature using remote sensing bands. We conducted experiments on real water quality datasets and random forest was chosen to be the core algorithm of the framework by comparing and analyzing the performance of different machine learning algorithms. The results show that among all tested machine learning models, random forest performs the best. The data prediction error rate of the random forest model in predicting the total nitrogen concentration in inland rivers was 4.9%. Moreover, to explore the prediction effect of random forest algorithm when the independent variable is non-water quality data, we took the reflectance of remote sensing bands as the independent variables and successfully inverted the salinity distribution of Shenzhen Bay in the Google Earth Engine (GEE) platform. According to the experimental results, the random forest-based water quality prediction framework can achieve 92.94% accuracy in predicting the salinity of nearshore waters.
format	article
author	Jianlong Xu Zhuo Xu Jianjun Kuang Che Lin Lianghong Xiao Xingshan Huang Yufeng Zhang
author_facet	Jianlong Xu Zhuo Xu Jianjun Kuang Che Lin Lianghong Xiao Xingshan Huang Yufeng Zhang
author_sort	Jianlong Xu
title	An Alternative to Laboratory Testing: Random Forest-Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies
title_short	An Alternative to Laboratory Testing: Random Forest-Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies
title_full	An Alternative to Laboratory Testing: Random Forest-Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies
title_fullStr	An Alternative to Laboratory Testing: Random Forest-Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies
title_full_unstemmed	An Alternative to Laboratory Testing: Random Forest-Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies
title_sort	alternative to laboratory testing: random forest-based water quality prediction framework for inland and nearshore water bodies
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/748aeb620b3b483e931c6a4bdeeb7d7b
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An Alternative to Laboratory Testing: Random Forest-Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies

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