Method for analyzing combined natural intensity of storm surges in multiple coastal areas

Abstract

The invention discloses a method for analyzing the combined natural intensity of storm surges in multiple coastal areas. The method includes the following steps of (1) statistically analyzing storm surges affecting multiple to-be-studied areas at the same time; (2) analyzing the water level and wave height of the storm surge in each single area; (3) build a multiple-area storm surge natural intensity multi-dimensional combined distribution model; (4) analyzing the combined natural intensity of the storm surges in the multiple areas. The method, by taking the combined recurrence period of the maximum water level and the effective wave height in a typhoon process of a single area as the storm surge natural intensity index, selecting the combined recurrence period of the wave height and the water level of each area as a random variable of edge distribution, considering the influence of typhoon frequency, constructing combined distribution of the natural intensity of the storm surges in multiple coastal areas, and further analyzing the probability of typhoon storm surge disaster at different locations simultaneously, is of great significance for coastal disaster prevention and mitigation, material allocation, and especially economic sustainable development.

Description

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AI Technical Summary

Benefits of technology

The technical effect that this new technology has been made overcomes previous methods by providing better results than previously possible or improving their overall performance compared to existing techniques.

Problems solved by technology

This patented technical problem addressed in this patents relates to studying how well two or more parts (such as sea walls) may react when exposed to severe environments such as tidal currents during an event that could cause catastrophees like floods. Existing techniques for analyzing these events involve calculating cumulative risks based on factors like time differences between multiple points along their trajectory over long periods of time. These approaches lack accuracy because they cannot accurately predict future changes in environmental situations affecting them.

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