Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

Author:

Chen Yujie1,Yao Ke1,Zhu Beibei1,Gao Zihao1,Xu Jie1,Li Yucheng1,Hu Yimin23,Lin Fei23ORCID,Zhang Xuesheng14

Affiliation:

1. School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China

2. Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China

3. Hefei Institutes of Collaborative Innovation for Intelligent Agriculture, Hefei 231131, China

4. Laboratory of Wetland Protection and Ecological Restoration, Anhui University, Hefei 230601, China

Abstract

Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized water bodies within rural areas is limited due to the low spatial resolution of remote sensing images. In this work, a typical small rural river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images and ground monitoring data of the river were obtained. Then, a comparative analysis of three univariate regression models and nine machine learning models (Ridge Regression (RR), Support Vector Regression (SVR), Grid Search Support Vector Regression (GS-SVR), Random Forest (RF), Grid Search Random Forest (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Catboost Regression (CBR)) for their accuracy in the prediction of turbidity (TUB), total nitrogen (TN), and total phosphorus (TP) was performed. TUB can be achieved by simple statistical regression models. The CBR model exhibited the best performance for the three index inversions on the test set evaluation metrics: R2 (0.90~0.92), RMSE (7.57 × 10−3~1.59 mg/L), MAE (0.01~1.30 mg/L), RPD (3.21~3.56), and NSE (0.84~0.92). The water pollution of the study area was closely related to its land-use pattern, excessive and irrational fertilizer application, and distribution of pollutant outlets.

Funder

National Natural Science Foundation of China

University Natural Science Research Project of Anhui Province

Open Project of the State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control

Feidong County Agricultural Non-Point Source Pollution Control Pilot Work Third Party Service Project

Publisher

MDPI AG

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