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Article

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

1
College of Engineering, Shantou University, Shantou 515000, China
2
Shantou Environmental Protection Monitoring Station, Shantou 515000, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Ryan Bailey and Mirko Castellini
Water 2021, 13(22), 3262; https://doi.org/10.3390/w13223262
Received: 27 September 2021 / Revised: 31 October 2021 / Accepted: 13 November 2021 / Published: 17 November 2021
(This article belongs to the Section Water Quality and Contamination)
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. View Full-Text
Keywords: water quality prediction; machine learning; total nitrogen; random forest; google earth engine water quality prediction; machine learning; total nitrogen; random forest; google earth engine
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MDPI and ACS Style

Xu, J.; Xu, Z.; Kuang, J.; Lin, C.; Xiao, L.; Huang, X.; Zhang, Y. An Alternative to Laboratory Testing: Random Forest-Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies. Water 2021, 13, 3262. https://doi.org/10.3390/w13223262

AMA Style

Xu J, Xu Z, Kuang J, Lin C, Xiao L, Huang X, Zhang Y. An Alternative to Laboratory Testing: Random Forest-Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies. Water. 2021; 13(22):3262. https://doi.org/10.3390/w13223262

Chicago/Turabian Style

Xu, Jianlong, Zhuo Xu, Jianjun Kuang, Che Lin, Lianghong Xiao, Xingshan Huang, and Yufeng Zhang. 2021. "An Alternative to Laboratory Testing: Random Forest-Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies" Water 13, no. 22: 3262. https://doi.org/10.3390/w13223262

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