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A method for zoning of mountain torrent disasters based on machine learning

A technology of machine learning and mountain torrents, applied in the direction of instruments, computer components, data processing applications, etc., can solve problems such as less attention to the study of mountain torrent disaster zoning

Active Publication Date: 2022-07-22
HOHAI UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

However, in recent years, the research on mountain torrent disasters has mostly focused on risk assessment, and less attention has been paid to the regionalization of mountain torrent disasters.

Method used

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  • A method for zoning of mountain torrent disasters based on machine learning
  • A method for zoning of mountain torrent disasters based on machine learning
  • A method for zoning of mountain torrent disasters based on machine learning

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Embodiment Construction

[0033] A method for zoning mountain torrent disasters based on machine learning described in this embodiment, the flow chart is as follows figure 1 shown, including the following steps:

[0034] (1) Preprocessing the impact factors of mountain torrent disasters, and extracting various short-term heavy rainfall indicators and underlying surface data indicators:

[0035] Because the original rainfall data has abnormal values ​​and data missing problems, it is necessary to preprocess the original rainfall data before use, remove the abnormal values, use interpolation to make up for the missing data, and finally obtain various short-term heavy rainfall indicators. The underlying surface data indicators with the same spatial resolution are obtained by resampling the underlying surface data. Build an index system that affects mountain torrent disasters, including short-term heavy rainfall factors, underlying surface data indicators, and small watershed attributes, and use these dat...

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Abstract

The invention discloses a mountain torrent disaster zoning method based on machine learning, comprising the following steps: (1) extracting various short-term heavy rainfall indicators and underlying surface data indicators; The key factors for screening mountain torrent disasters in the data indicators; (3) constructing the first-level spatial clustering of mountain torrent disasters based on the self-organizing mapping neural network model SOM, and then performing second-level clustering; (4) using external indicators and internal indicators to analyze mountain torrents The secondary clustering results of the disaster are comprehensively evaluated to determine the best clustering scheme for mountain torrent disasters; (5) the standard deviation rate of the area of ​​the zonal patch is calculated, and the small patches are merged and processed to obtain the final zoning result of the mountain torrent disaster. The invention selects the key factors related to the mountain torrent disaster by using the random forest model, then uses the SOM to perform two-level mixed clustering, and realizes the spatial division of the mountain torrent disaster by combining with multiple machine learning methods.

Description

technical field [0001] The invention relates to the field of mountain torrent disaster prevention and control, in particular to a mountain torrent disaster zoning method based on machine learning. Background technique [0002] Mountain torrent is the rapid rise and fall of surface runoff caused by short-term heavy rainfall in small watersheds in hilly areas. Mountain torrent disaster is one of the deadliest natural disasters in the world. Serious and other characteristics, often causing casualties, destroying buildings and facilities, changing the shape of the river and destroying the natural environment. With the intensification of extreme climate events and human activities, the issue of mountain torrents has gradually attracted widespread attention and great attention from all over the world. The development of mountain torrent disaster zoning can effectively reflect the spatial differentiation pattern of mountain torrent disasters, provide a scientific basis for the pre...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/06G06Q50/26G06K9/62
CPCG06Q10/0635G06Q10/06393G06Q50/26G06F18/23G06F18/24323Y02A10/40
Inventor 陈跃红张若婧张晓祥
Owner HOHAI UNIV
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