Flood routing process flooding range measurement method based on deep learning

A technology of inundation range and deep learning, which is applied in the field of inundation range measurement based on the deep learning process of flood evolution. The effect of small recognition error and good accuracy
CN112001964AInactive Publication Date: 2020-11-27XIAN UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
XIAN UNIV OF TECH
Publication Date
2020-11-27
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a flood routing process flooding range measurement method based on deep learning. The method comprises the steps that firstly, a camera is arranged to be used for collecting video data of a whole test river channel; calibrating the camera through a camera checkerboard calibration method to correct the perspective distortion effect; extracting image data at different time points from the video data; constructing a flood test flooding range sample library; carrying out primary labeling on the sample through a Label labeling tool; and finally, adopting an MASK R-CNN imageinstance segmentation algorithm to realize automatic segmentation and recognition of a flooding range, and obtaining the submerging range change of the whole test riverway by splicing the recognitionpictures. The method has the advantages of being low in economic cost, high in intelligent degree, high in efficiency, high in precision, high in applicability and the like, and therefore the method can be used for extracting the obtained flooding range data in the flood routing test.
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Description

technical field

[0001] The invention belongs to the technical field of data monitoring, and relates to a method for measuring the submerged range of a flood evolution process based on deep learning. Background technique

[0002] As a natural disaster, flood has a high probability of occurrence, a wide range, and great harm, which may cause heavy casualties and property losses. Numerical models are increasingly used to simulate flood propagation more and more widely. However, the verification of these models is mainly based on the comparison of different model predictions, or the water depth data of individual survey points from field surveys and experiments, and lacks data on the spatial variation of inundation range during flood evolution.

[0003] In the traditional flood evolution process, the water level sensors and current velocity meters arranged in the river channel are mainly used to monitor the data of individual measuring points. This method can only monitor the ...

Claims

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