A flood disaster assessment method

CN117236154BActive Publication Date: 2026-11-03CHENGDU UNIV OF INFORMATION TECH +1
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Patent Information

Application Number
CN202310809847.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-07-03
Publication Date
2026-11-03
Estimated Expiration
2043-07-03

AI Technical Summary

Technical Problem

Existing methods for assessing flood damage ignore the complex nonlinear relationship between rainstorm flood losses and influencing factors, and the reliance on statistical data based on administrative regions leads to inaccurate assessments, failing to reflect the uneven distribution of floodwaters within a region.

Method used

A disaster index prediction model is constructed by using convolutional neural networks to extract disaster features from three aspects: spatial, nonlinear and temporal. A training dataset is built using historical flood disaster data. Precipitation features are extracted through forget gate, input gate and output gate, and prediction is performed through convolutional modules and fully connected layers. Disaster assessment is carried out by combining raster data.

Benefits of technology

It enables rapid and accurate assessment of flood disasters, comprehensively evaluates the severity of disasters, provides effective disaster prevention and mitigation measures, and quickly calculates disaster indices in various regions when disasters occur, providing a scientific basis for emergency response.

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Abstract

The application discloses a flood disaster assessment method, which comprises the following steps: firstly, a plurality of training data sets are constructed by using historical flood disaster data; then, a plurality of parallel disaster index prediction models are constructed, so that each training data set is used for training a disaster index prediction model; after the disaster index prediction model is trained, disaster-related features are extracted from the aspects of space, nonlinearity and time sequence by using the disaster index prediction model, and the disaster index is accurately predicted; and then, according to the grade division of the disaster index, the corresponding emergency response measures are matched.
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