A Comprehensive Review of Assessing Storm Surge Disasters: From Traditional Statistical Methods to Artificial Intelligence-Based Techniques

Author:

Zhang Yuxuan1,Zhang Tianyu123

Affiliation:

1. College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China

2. Laboratory for Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China

3. Key Laboratory of Climate, Resources and Environment in Continental Shelf Sea and Deep Sea of Department of Education of Guangdong Province, Guangdong Ocean University, Zhanjiang 524088, China

Abstract

In the context of global climate change and rising sea levels, the adverse impacts of storm surges on the environment, economy, and society of affected areas are becoming increasingly significant. However, due to differences in geography, climate, and other conditions among the affected areas, a single method for assessing the risk of storm surge disasters cannot be fully applicable to all regions. To address this issue, an increasing number of new methods and models are being applied in the field of storm surge disaster risk assessment. This paper introduces representative traditional statistical methods, numerical simulation methods, and artificial intelligence-based techniques in this field. It compares these assessment methods in terms of accuracy, interpretability, and implementation difficulty. The paper emphasizes the importance of selecting appropriate assessment methods based on specific conditions and scientifically combining various methods in practice to improve the accuracy and reliability of storm surge disaster risk assessments.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Innovative Team Plan for Department of Education of Guangdong Province

Guangdong Science and Technology Plan Project

Independent research project of Southern Ocean Laboratory

Guangdong Ocean University Scientific Research Program

Publisher

MDPI AG

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