Inversion and long-term series analysis of turbidity in inland rivers and lakes based on XGBoost
Turbidity is one of the key elements affecting the underwater light field and nutrient cycling.Turbidity monitoring can provide a scientific basis for pollution prevention,control,and early warning of river and lake water quality.The typical rivers and lakes in the demonstration zone of the Yangtze River Delta were taken as the study area.The turbidity inversion model was constructed using in-situ data,based on which the long-term dynamic changes of turbidity in the rivers and lakes of the study area were analyzed using a total of 323 Landsat TM/ETM+/OLI images from 1984 to 2022.Through the comparison between the traditional empirical model,semi-empirical model,and machine learning model,the machine learning model named XGBoost demonstrated the highest accuracy(R2 and RMSE were 0.68 and 4.78 NTU,respectively).The results of turbidity inversion showed that,in the last 40 years,turbidity in the river channel and the northern non-fishing area of Dianshan Lake increased by 10％and 12％,respectively,while turbidity in Yuandang Lake and Dalian Lake decreased by 19％and 27％,respectively.Moreover,it was found that turbidity increased with the expansion of the built-up land area.The seasonal variation of turbidity in the study area was significant and the average turbidity in autumn and winter was 6 NTU higher than that in spring and summer.The monthly average turbidity was negatively correlated with the monthly average precipitation(r=-0.61,p＜0.05),but its correlation with the monthly average wind speed was found to be insignificant.The XGBoost-based long-term inversion of turbidity from Landsat images can not only capture the spatiotemporal trend of turbidity in the study area,but also reveal the direction of water pollution management and control,eventually contributing to the integrated development of the Yangtze River Delta.
turbidityXGBoostLandsatlong-term seriesdemonstration zone of the Yangtze River Delta
国家科技期刊平台
NSTL
万方数据
