Flood forecasting method based on CS-LSTM

Abstract

The invention provides a flood forecasting method based on CS-LSTM, which is used for solving the technical problem of poor prediction performance of a model caused by inaccurate parameter selection in an existing prediction model. The method comprises the steps that firstly, collecting characteristic parameters of a target hydrological station to serve as sample data, performing normalization processing on the sample data, and dividing the normalized sample data into a training set and a verification set according to the time sequence; secondly, constructing a flood forecasting model based ona long-short-term memory neural network, and performing iterative optimization on the flood forecasting model by utilizing a cuckoo search algorithm, a training set and a verification set to obtain afinal flood forecasting model; and finally, inputting the to-be-predicted characteristic parameters into the final flood forecasting model to obtain a prediction result. According to the method, theflood process in the future prediction period can be accurately predicted, a certain basis can be provided for hydrology and water resource management and reservoir scheduling, and a reference is provided for application of deep learning in the hydrology field.

Description

technical field [0001] The invention relates to the technical field of hydrological forecasting, in particular to a flood forecasting method based on CS-LSTM. Background technique [0002] Flood disasters threaten the safety of people's lives and property and hinder the sustainable development of social economy. Accurate flood forecasting is an important non-engineering measure for flood control and disaster reduction. At present, hydrological forecast research is mainly to establish traditional hydrological models based on physical mechanisms or physical concepts for river basins. The establishment of traditional hydrological models usually requires complex mathematical formulas and has its applicable conditions, so it will be limited by many factors, resulting in the flood process. The forecast is poor. With the development of science and technology, the ways of obtaining data are gradually diversified, such as obtaining land use type, vegetation type and meteorological c...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the deficiencies in the above-mentioned background technology, the present invention proposes a flood forecasting method based on CS-LSTM, ...

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