Monitoring data and model fusion-based water monthly-scale water quality pre-judgment method

Abstract

The invention relates to a monitoring data and model fusion-based water body monthly-scale water quality pre-judgment method, and belongs to the technical field of water environment management, and the method comprises the steps: building a hydrological water quality model of a drainage basin where a target water body is located based on an LSPC model; building a hydrodynamic force-water quality-water ecology model of the target water body on the basis of IWIND-LR; constructing a weather forecast scene generation module pf an uncertainty hour-by-hour weather forecast database based on historical data; constructing a monitoring data and data fusion module based on ensemble Kalman filtering; and establishing a monthly-scale water quality pre-judgment calculation system of the target water body, and simulating and obtaining a water quality simulation result of the target water body in the next month. The method has the advantages that the initial field of the target water body is generated by adopting a data and model fusion method, the influence of the initial field of the model on a simulation result is corrected, and the reliability and accuracy of a monthly-scale water quality pre-judgment result are improved.

Description

technical field [0001] This application relates to the technical field of water resources management, and in particular to a monthly-scale water quality prediction method based on the fusion of monitoring data and models, which is applicable to water quality prediction and assessment of rivers, reservoirs, lakes and other waters in the next month. Background technique [0002] The water quality of lakes, reservoirs and other water bodies is directly related to the sustainable development of the water environment and water ecology. Predicting and predicting the water quality of lakes and reservoirs and grasping the changing trend of water quality in advance are very important for scientific decision-making of water environment management. This is because the water quality response lags behind the management measures. Only by identifying the possible trend of water quality with a sufficient amount of time in advance, can early response measures be taken, so that the management ...

AI Technical Summary

Problems solved by technology

[0003] Water pollution has the characteristics of extensiveness, randomness, uncertainty, and difficulty in monitoring. The existing water quality prediction methods cannot take into account the uncertainty of weather forecast and the influence of the initial field at the same time, resulting in insufficient time advance of prediction analysis, The accuracy of water quality prediction is low

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