Flood prediction model, information processing method, storage medium and computer equipment

Abstract

The invention belongs to the technical field of flood prediction, and discloses a flood prediction model, an information processing method, a storage medium, and computer equipment. The flood prediction model comprises a spatial-temporal feature abstraction layer used for extracting features from terrain-rainfall spatial-temporal features, a feature fusion layer used for combining space-time features abstracted by the convolutional network with historical trend features through Concat connection operation, and a prediction output layer used for predicting the flow change condition in the future T hours. The method aims to solve the problems that a traditional model needs a large number of parameter calibration and a data driving model cannot accurately predict the flood process. A convolutional neural network CNN based on two-dimensional convolution is introduced into the field of flood forecasting, the feature abstraction capability of the CNN is utilized, rainfall space-time distribution features, landform features and flow change trend features are fused, and a flood forecasting model with the forecasting period of 24 hours and 36 hours is constructed. Through inspection, the model meets the requirements of flood forecasting.

Description

technical field [0001] The invention belongs to the technical field of flood forecasting, and in particular relates to a flood forecasting model, an information processing method, a storage medium and computer equipment. Background technique [0002] At present, flood disasters usually cause a large number of casualties and property losses. According to statistics, 40% of economic losses are usually caused by floods. Therefore, accurate prediction of river water level is crucial to public safety and management of hydrological water resources. With the enhancement of awareness of flood control and the development of science and technology, countries have invested a lot of human and financial resources to improve the ability of flood forecasting. In fact, the prediction of flood water level has always been a focus of attention. The formation of flood process is affected by many factors such as rainfall, topography, vegetation, soil and evaporation, and is a complex nonlinear ...

AI Technical Summary

Problems solved by technology

[0005] (1) Traditional hydrological forecasting models often require personnel with professional background knowledge and experience to calibrate parameters, which greatly reduces the efficiency of forecasting

[0006] (2) The values ​​of the parameters of the traditional hydrological forecasting model are different in different regions, and even some parameters cannot be obtained directly. Therefore, the default parameters are used in the actual use process, which greatly affects the accuracy of the forecast.

Therefore, these models cannot realize the prediction of flood peak and peak arrival time, and cannot fully meet the actual flood forecast requirements.

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