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Mountain torrent disaster zoning method based on machine learning

A machine learning and mountain torrent technology, applied in the fields of instruments, computer parts, data processing applications, etc., can solve the problem of less attention to the zoning research of mountain torrent disasters

Active Publication Date: 2022-03-15
HOHAI UNIV
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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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  • Mountain torrent disaster zoning method based on machine learning
  • Mountain torrent disaster zoning method based on machine learning
  • Mountain torrent disaster zoning method based on machine learning

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Embodiment Construction

[0033] A kind of flash flood disaster zoning method based on machine learning described in this embodiment, the flowchart is as follows figure 1 shown, including the following steps:

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

[0035] Due to the problems of outliers and missing data in the original rainfall data, it is necessary to preprocess the original rainfall data before use, remove the outliers, make up for the missing data by interpolation, and finally obtain various short-term heavy rainfall indicators. The underlying surface data are resampled to obtain underlying surface data indicators with the same spatial resolution. Construct 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 data as input factors that are...

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Abstract

The invention discloses a mountain torrent disaster zoning method based on machine learning. The mountain torrent disaster zoning method comprises the following steps: (1) extracting various short-time heavy rainfall indexes and underlying surface data indexes; (2) screening key factors of mountain torrent disasters from the short-time heavy rainfall index and the underlying surface data index; (3) constructing a self-organizing mapping neural network model (SOM) to perform primary spatial clustering on the mountain torrent disasters, and then performing secondary clustering; (4) comprehensively evaluating the secondary clustering result of the mountain torrent disasters by using the external indexes and the internal indexes, and determining an optimal clustering scheme of the mountain torrent disasters; and (5) calculating the standard deviation rate of the area of the zoning pattern spots, merging and processing the tiny patches, and obtaining a final mountain torrent disaster zoning result. According to the method, key factors related to mountain torrent disasters are selected through a random forest model, then two-stage hybrid clustering is carried out by utilizing SOM, and mountain torrent disaster space division is realized by combining various machine learning methods.

Description

technical field [0001] The invention relates to the field of mountain torrent disaster prevention and control, in particular to a method for zoning mountain torrent disasters based on machine learning. Background technique [0002] Mountain torrents are surface runoff that rises and falls rapidly in small watersheds in hilly areas due to short-duration heavy rainfall. Mountain torrents are one of the deadliest natural disasters in the world. They are sudden, concealed, destructive, and costly Serious and other characteristics, often causing casualties of residents, destroying building facilities, changing the shape of rivers and destroying the natural environment. With the intensification of extreme climate events and human activities, the problem of mountain torrent disasters 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 patter...

Claims

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Application Information

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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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