Flood prediction method based on an attention model long-short-term memory network

A long- and short-term memory and attention model technology, applied in forecasting, data exchange networks, neural learning methods, etc., can solve problems such as reducing the accuracy of flood forecasting, and achieve the effect of shortening the average forecast time and increasing the accuracy.

Active Publication Date: 2019-04-05
HOHAI UNIV
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AI Technical Summary

Problems solved by technology

[0004] The invention provides a flood prediction method based on attention model long-short-term memory network, which solves the problem of noise caused by meaningless flood factors in traditional methods and reduces the accuracy of flood prediction

Method used

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  • Flood prediction method based on an attention model long-short-term memory network
  • Flood prediction method based on an attention model long-short-term memory network
  • Flood prediction method based on an attention model long-short-term memory network

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Embodiment Construction

[0032] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0033] Such as figure 1 As shown, the flood prediction method based on the attention model long short-term memory network includes the following steps:

[0034] Step 1. Collect flood-related data, which includes flow at different time points and values ​​of related flood factors at this time.

[0035] Step 2, introduce the attention model into the long short-term memory network, and construct the long short-term memory network based on the attention model.

[0036] The specific process is as follows:

[0037] 21) Use the long-short-term memory network structure as the basic structure of the long-short-term memory network based on the attention model.

[0038] The unit of the long short-term ...

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Abstract

The invention discloses a flood prediction method based on an attention model long-short time memory network, which comprises the following steps: collecting flood related data including flows and flood factors at different time points; introducing An attention model into the long-short term memory network, and constructing a long-short term memory network based on the attention model; Training the attention model-based long-term and short-term memory network by using the standardized flood related data; And carrying out flood prediction by using the trained long-short term memory network based on the attention model. According to the method, the sequence modeling capability of an original long-short-term memory network is reserved, an attention model is introduced, a circulation scheme isused for optimization, local situation information is described as a weight scheme, some flood factors which are not used for prediction are ignored, the prediction average time is greatly shortened,and meanwhile accuracy is improved.

Description

technical field [0001] The invention relates to a flood prediction method based on attention model long-short-term memory network, which belongs to the field of flood prediction. Background technique [0002] As one of the most common and scattered natural disasters, floods often bring devastating disasters to people. If we could accurately predict floods by predicting sequence flow values ​​in advance, hundreds of lives and property could be saved. [0003] Over the past decade, researchers from the pattern recognition and hydrology communities have proposed a variety of approaches to construct accurate and robust flood forecasting models, which fall into two categories, hydrological models and data-driven models, with data-driven models now commonly used The driving model is usually based on historically collected flood factors (such as previous rainfall, river runoff, etc.) to estimate river flow, but not all of the collected flood factors are representative and meaningf...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06Q10/04H04L12/24
CPCH04L41/145G06N3/08G06Q10/04G06N3/047G06N3/048Y02A10/40
Inventor 巫义锐王晓珂徐维刚冯钧
Owner HOHAI UNIV
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