Waterlogging prediction method and system and storage medium

Abstract

The invention relates to a hydrological prediction technology, in particular to a waterlogging prediction method and system and a storage medium, and the method comprises the steps: obtaining basic data, carrying out the generalization of a drainage basin and hydraulic facilities, and calibrating model parameters; constructing an SVM model, and predicting an urban waterlogging space-time range and a waterlogging submerging depth; constructing a pipe network hydrodynamic model of the research area as an AGswmm model, and simulating the waterlogging condition of the research area in combination with topographic data; training an SVM model by using simulation result data generated by the AGswmm model to realize model coupling so as to obtain an urban inland inundation rainfall runoff prediction model; performing data assimilation, calculating the prediction model by taking actually measured and predicted rainfall as input conditions, performing error analysis on actually measured data and prediction data of the prediction model to obtain a residual error, and correcting the residual error to obtain a prediction result closest to a real value. According to the method, model errors are reduced, the waterlogging simulation precision is ensured, and meanwhile, the processing and calculating time of mass hydrological data is shortened.

Description

technical field [0001] The invention relates to hydrological forecasting and forecasting technology, specifically a waterlogging forecasting method, system and storage medium combined with big data and traditional hydrodynamic calculations. Background technique [0002] (1) Traditional hydrodynamic model [0003] Since the 1970s, developed countries have begun to do research on urban storm drainage. After nearly half a century of development, experts and scholars in developed countries have put forward many valuable research results on urban drainage models. The more famous ones are SWMM. , SewerGEMS, STORM, Wallingford Model, MIKE URBAN, Illinois Urban Drainage Area Simulator (ILLUDAS), revised RRL (Road Research Laboratory) model, HSPF (Hydrological Simulation Program-Fortan), MOUSE (Modeling of Urban Sewer) , UCURM (The University of Cincinnati Urban Runoff Model) and other models also have certain applications in practice. MH Hsu et al. combined SWMM model, two-dimensi...

AI Technical Summary

Problems solved by technology

[0010] It can be seen that whether it is a traditional hydrodynamic model, a machine learning model, or a data assimilation algorithm, each has its own strengths and weaknesses in hydrological forecasting or prediction of urban drainage or water accumulation; Large error, forecast response speed and forecast accuracy need to be improved

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