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

Abstract

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.

Developing and validating a multivariable prediction model for predicting costs of colon surgery

“…Another similar approach to comparing algorithms was made by Xu et al (2021), which developed and tested an accurate prediction model based on the random forest classification algorithm (19). They evaluated the prediction for inland water quality.…”

“…The results in the study by Wang et al (2020) align with those of this study since it was also established that the random forest model is the most accurate compared to other models. The study by Wang et al (2020) provides better insight into the accuracy of the random forest model because it compared it to multiple models (19). It is an indication that the random forest model is one of the most accurate prediction models that can be used to predict costs for surgery.…”

Developing and validating a multivariable prediction model for predicting the cost of colon surgery

“…Another similar approach to comparing algorithms was made by Xu et al, who developed and tested an accurate prediction model based on the random forest classification algorithm ( 19 ). They evaluated the prediction for inland water quality.…”

“…The results in the study by Wang et al align with those of this study since it was also established that the random forest model is the most accurate compared to other models. The study by Wang et al provides better insight into the accuracy of the random forest model because it compared it to multiple models ( 19 ). It indicates that the random forest model is one of the most accurate prediction models that can be used to predict costs for surgery.…”

Prediction of Quality of Water According to a Random Forest Classifier

“…The aime of Xu, et al research [12] is to design a framework for the prediction of the water quality in two The collected samples made a total of 1917 creating the dataset which was then subjected to normalization and correlation analysis. 90% of these data were used to train machine learning algorithms including Decision Tree, KNN, SVR, MLR, Random Forest, Ridge Regression, and GBRT.…”