Water conservancy big data-based drainage basin flood runoff forecasting method

Abstract

The invention relates to the technical field of flood forecasting, in particular to a drainage basin flood runoff forecasting method based on water conservancy big data, which comprises the following steps: collecting data, establishing a database, acquiring meteorological data, satellite remote sensing image data and the like of a target drainage basin by a cloud, collecting flood data of the target drainage basin over the years, and establishing flood big data of the target drainage basin; the designed net rain and rainfall duration of the peak flood flow are calculated, a PDF matching method is adopted to correct radar estimation, and the systematic deviation of rainfall is inversed by a satellite; establishing a model; performing model experiment result and analysis, and performing analysis, forecasting and early warning on the experiment result based on rainfall big data and a model test process; based on artificial intelligence and big data technologies, a new-generation distributed hydrological model which takes a small watershed as a calculation unit and integrates a model library, an artificial intelligence algorithm library, a parameter library and a priori knowledge library is constructed, so that the process of timely forecasting and early warning of watershed flood runoff based on water conservancy big data is achieved.

Description

technical field [0001] The invention relates to the technical field of flood forecasting, in particular to a flood runoff forecasting method based on water conservancy big data. Background technique [0002] Affected by climate warming, the frequency of El Niño phenomenon has increased, causing the rainfall in different regions to gradually become severe and frequent, and it is easy to cause flood disasters in the mountains, which seriously threatens and damages the infrastructure and people's property safety in the disaster-stricken areas. In order to prevent flood disasters, it is necessary to carry out statistical analysis on the characteristics of flood disasters in different regions, so that the design method of the statistical system of flood disaster characteristics has become a research hotspot. The problems of low efficiency and low statistical accuracy require research on the design method of the flood disaster characteristic statistical system. At present, in the ...

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Problems solved by technology

[0002] Affected by climate warming, the frequency of the El Niño phenomenon has increased, leading to increasingly severe and frequent rainfall in different regions, which is prone to flood disasters in mountainous areas, seriously threatening and damaging the infrastructure and people's property safety in the affected areas. In order to prevent flood disasters, it is necessary to carry out statistical analysis on the characteristics of flood disasters in different regions, so that the design method of flood disaster characteristics statistical system has become a research hotspot. The problems of low efficiency and low statistical accuracy require research on the design method of the flood disaster characteristic statistical system. At present, in terms of flood forecasting in the basin, the traditional flood forecasting model is to collect rainfall and hydrological data by burying sensors in the river basin and building telemetry stations. And through the communication system, the data is sent back to the central station for analysis and processing, and then the flood forecast is carried out according to the analysis results. At present, the basin flood forecast is developing in the direction of forecasting based on remote sensing big data, applying the theoretical method based on remote sensing big data, combined with mountain torrents Disaster prevention investigation and evaluation project construction results, completing the establishment of the basin flood forecasting model is the focus and difficulty of project research, so this invention proposes a basin flood runoff forecasting method based on water conservancy big data

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