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

A BP neural network and deep learning technology, which is applied in the field of flood prediction based on deep learning models and BP neural network corrections, can solve problems such as complex models, poor model adaptability, and difficult calibration of model parameters, so as to improve the accuracy rate , easy to set up, and improve the effect of peak time and peak forecast accuracy

Active Publication Date: 2019-01-25
HOHAI UNIV
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Problems solved by technology

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

Method used

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

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Experimental program
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Embodiment

[0079] In order to verify the effect of the present invention, the Tunxi River Basin in Anhui Province was selected, and a model was built to predict the flood process in the watershed. The catchment area of ​​the watershed is 2696.76 square kilometers. It is located in a subtropical monsoon climate, with a suitable climate, an annual average temperature of 17°C, and abundant rainfall. The data of 33 floods that occurred from 1982 to 2002 were selected as the research data of the experiment. All data have been compiled and processed through hydrological data, and there is no missing data.

[0080] The following is a comparative experiment from the two aspects of the forecast period and real-time correction results, to analyze the experimental results and test the usability of the model.

[0081] 1) Starting from the foreseeable period of the experimental model. Taking the forecast period as the starting point to design a comparative experiment, carry out traffic forecasting, ...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26G06N3/08
CPCG06N3/084G06Q10/04G06Q50/265Y02A10/40
Inventor 王继民朱跃龙张成张鹏程朱晓晓张玲
Owner HOHAI UNIV
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