期刊
ECOLOGICAL INDICATORS
卷 133, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.ecolind.2021.108434
关键词
UAV remote sensing; Water quality inversion; Machine learning; Urban river
资金
- National Natural Science Foundation of China [41901282, 41971311]
- National Natural Science Foundation of Anhui [2008085QD188]
- Science and Technology Major Project of Anhui Province [201903a07020014]
- Key Research and Development Project of Anhui Province [202104b11020022]
- Engineering Center for Geographic Information of Anhui Province
This paper utilizes unmanned aerial vehicle images and water quality data to model water quality parameters in urban rivers using the GA_XGBoost algorithm. The results show high precision and consistency with measured data, with the GA_XGBoost model outperforming other algorithms. This method has significant practical implications for advancing intelligent water environment monitoring technology in urban areas.
Urban rivers play an essential role in the human environment and urban development; because of their narrow and long characteristics, challenging for general remote sensing data sources to meet the monitoring requirements. In order to solve the problem of insufficient application of remote sensing water quality monitoring in urban rivers. In this paper, based on unmanned aerial vehicles (UAV) images and measured water quality data, the genetic algorithm_extreme gradient boosting (GA_XGBoost) algorithm is used to model water quality parameters in the study area, combined with its characteristics of supporting urban river polymorphism learning and semantic feature analysis. The results show that the coefficient of determination (R2) of GA_XGBoost algorithm for chlorophyll a (Chla), total phosphorous (TP), total nitrogen (TN), ammonia-nitrogen (NH3-N) and turbidity (TUB) is 0.855, 0.699, 0.787, 0.694, and 0.597, respectively, indicating a high precision and the predicted results are consistent with the measured data. Meanwhile, this paper compares the GA_XGBoost model with other algorithms: Deep Neural Network (DNN), Random Forest, genetic algorithm_RandomForest (GA_RandomForest), adaptive boosting (AdaBoost) and genetic algorithm_adaptive boosting (GA_AdaBoost), and the performance of the GA_XGBoost model is better. At the same time, data from different periods have been added to verify the model's applicability. Moreover, based on the inversion results, analyze from the point of view of point source pollution, non-point source pollution, etc., to further investigate the influencing factors that cause urban river pollution. The current method has important practical significance for promoting the intelligent and automatic level of water environment monitoring technology in ecological environmental protection and urban water resources protection.
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