A flood forecasting method based on depth learning model and BP neural network correction

Abstract

The invention discloses a flood forecasting method based on depth learning model and BP neural network correction, including: 1.normalizing the historical flood process data, 2. analyzing the normalized historical flood process data to obtain the influence time of each monitoring point on the outlet flow; 3. establishing the input and output values of the forecasting model from the historical flood data by using the sliding window, and establishing the training data set TRSet1 of the model, 4. using TRSet1 to train and establish the flood forecasting model CNNFM; 5, establishing a real-time error correction model training data set TRSet2; 6. training and establishing BP neural network based error correction model BPCM by TRSet2, 7. forecasting the real-time data with CNNFM and correcting them with BPCM to get the final forecast value for the real-time monitoring flow and rainfall. The invention models the training data through the characteristics of automatic extraction of data features by depth learning, and carries out real-time correction through BP neural network to improve the accuracy of model prediction.

Description

technical field [0001] The invention belongs to the technical field of information processing, in particular to a flood prediction method based on a deep learning model and BP neural network correction. Background technique [0002] my country has a vast territory, abundant river water resources, and frequent flood disasters, which have hindered my country's economic development and social progress. Therefore, it is very important to carry out research on hydrological forecasting. Traditionally, conceptual hydrological models based on physical processes are mostly used to describe hydrological processes. This type of method is relatively mature and can achieve better prediction results. However, these models are complex, poorly adaptable to different regions, and difficult to calibrate model parameters. Therefore, data-driven hydrological process prediction methods are increasingly developed. In recent years, my country has established a relatively complete hydrological in...

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However, these models are complex, poorly adaptable to different regions, and difficult to calibrate model parameters

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