Study On Water Quality Evaluation And Prediction Of Taolinkou Reservoir

In recent years,the problem of water pollution in China has become more and more serious.The health status of water environment is not optimistic.It is especially important to ensure the supply of water in surface water sources.Water environment management is imperative.This paper combines the provincial science and technology plan project(17273905D)The application of the system in the ecological safety warning of surface water reservoirs is based on the Taolingkou Reservoir of the large-scale water conservancy project,and the water quality assessment,water quality prediction and water quality warning are discussed in depth.Using the measured data of the Huangdao Taolinkou Reservoir in 2008-2017,six indicators of p H,total phosphorus(TP),ammonia nitrogen(NH3-N),permanganate index(CODMn),dissolved oxygen(DO)and five-day biochemical oxygen demand(BOD5)were selected.Water quality evaluation was carried out by using three methods: single factor evaluation method,comprehensive pollution index method and BP neural network model.(1)The single-factor water quality evaluation results showed that the water quality all-effect evaluation of Taolinkou Reservoir meets the requirements of Class III water quality targets.(2)The comprehensive pollution index method showed that the water quality status of the water source station and the outbound station are all light pollution.Three water quality indicators such as TP,CODMn and BOD5 have a greater impact on the water quality of Taolinkou Reservoir.(3)BP neural network model shows that the average annual water quality of the Taolinkou Reservoir is Grade II water quality.In the water quality prediction study,based on the BP neural network model and the WASP water quality model,the water quality indicators of the outbound station are predicted by the measured data of the upstream water source station of the Taolinkou Reservoir.The simulation results showed that the BP neural network model determines the coefficient(R2)and nash efficiency coefficient(NSE)are larger than the WASP water quality model,and the relative error and root mean square error(RMSE)are lower than the WASP water quality model.It could be seen that the prediction accuracy of the BP neural network model is higher.Finally,according to the data of the water source station monitored by the “Blue Guardian” system in real time,the BP neural network model is used to predict the water quality status of the outbound station in advance,and then the geographic information system(GIS)is used to realize the real-time visualization of water quality warning.