Remote sensing technology is an effective method for monitoring the water quality of inland water.Dongzhen Reservoir serves as the water source for Putian City.In order to achieve real-time water quality monitoring and understand its spatial distribution,this paper is based on Landsat 8 OLI remote sensing images,in combination with measurements of Chl-a,Turbidity,and COD concentrations from 124 sampling points.Statistical regression models,BP neural network models,and XGBoost models were constructed and evaluated using precision tests such as R2,MAE,and RMSE.The results indicate that the BP neural network model outperforms the statistical regression model with an R2 value exceeding 0.9,albeit with some risk of overfitting.On the other hand,the XGBoost model effectively mitigates overfitting,demonstrating robust fitting capabilities and high predictive accuracy.
关键词
Landsat/8/OLI/水质参数/BP神经网络模型/XGBoost模型
Key words
Landsat 8 OLI/water quality parameters/BP neural network model/XGBoost model
维普
万方数据