Flood forecasting method for area without runoff data

Abstract

The invention discloses a flood forecasting method for an area without runoff data. For a completed drainage basin with historical runoff data and a better forecasting effect, model parameters of the drainage basin and feature information, subjected to cluster processing, of the drainage basin are stored in a database in advance; when the area without the runoff data is forecast, firstly the feature information of the underlying surface in the drain basin is automatically subjected to cluster processing, then drain basins similar to each model parameter are searched one by one in a large drain basin where the drain basin is located, correlation analysis is performed on the model parameters of similar drain basins and predetermined feature information, and finally each parameter is determined. According to the flood forecasting method, based on accumulation of a large number of drain basin forecast data, automatic selection of the similar drain basins of the to-be-forecast area without the data, and automatic correlation analysis and final determination of the model parameters are completed, artificial subjective judgment errors can be effectively avoided, the efficiency is improved, and a foundation is laid for forecasting in a large area without the data.

Description

technical field [0001] The invention relates to a flood forecasting method in an area without runoff data, and belongs to the technical field of flood forecasting. Background technique [0002] Hydrological processes in nature are extremely complex and affected by many factors. Generally, generalized hydrological models are used for prediction. On the basis of , determine the hydrological model parameters of the current forecast basin. In most watersheds, the hydrological model parameters are calculated by simulating a large amount of historical runoff data (substituting information such as rainfall and evaporation into the model, and adjusting the parameters to make the calculated runoff process consistent with the actual situation); , the historical runoff data sequence is very short, which cannot meet the forecasting requirements; at the same time, due to human activities and other influences, the characteristics of runoff production and confluence in the basin have chan...

AI Technical Summary

Problems solved by technology

In fact, each parameter in the hydrological model represents a different physical meaning. Because each watershed has its own uniqueness, it is difficult to find a watershed with similar characteristics to all the parameters in the model, and the existing studies are only based on one or Several characteristic variables look for reference watersheds, but all parameters are transferred uniformly or use the same watersheds, which cannot reflect the uniqueness of parameters

[0007] Subjective discrimination and low efficiency: the existing technologies all use manual discrimination and processing to try to solve the problem of flood forecasting in areas without data. The technology is difficult to promote in large quantities, and it contains human subjectivity, which is inefficient

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