Research progress and considerations on short-term prediction of extreme wind fields based on machine learning

TAO Tianyou; DENG Peng; WANG Hao; FAN Yansheng

doi:10.7638/kqdlxxb-2025.0041

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Research progress and considerations on short-term prediction of extreme wind fields based on machine learning

更新时间：2026-05-28

- Research progress and considerations on short-term prediction of extreme wind fields based on machine learning
- Vol. 43, Issue 5, Pages: 78-91(2025)
- 作者机构：
  
  1. 东南大学混凝土及预应力混凝土结构教育部重点实验室,南京,211189
  2. 东南大学土木工程学院,南京,211189
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2025.0041
  CLC： P456.1;TU312.1;TP181
- Published：2025
- 稿件说明：
移动端阅览
陶天友, 邓鹏, 王浩, 等. 基于机器学习的极端风场短时预测研究进展与思考[J]. 空气动力学学报, 2025, 43(5): 78−91.

TAO Tianyou, DENG Peng, WANG Hao, et al. Research progress and considerations on short-term prediction of extreme wind fields based on machine learning[J]. 2025, 43(5): 78-91.
陶天友, 邓鹏, 王浩, 等. 基于机器学习的极端风场短时预测研究进展与思考[J]. 空气动力学学报, 2025, 43(5): 78−91. DOI： 10.7638/kqdlxxb-2025.0041

TAO Tianyou, DENG Peng, WANG Hao, et al. Research progress and considerations on short-term prediction of extreme wind fields based on machine learning[J]. 2025, 43(5): 78-91. DOI： 10.7638/kqdlxxb-2025.0041.

摘要

近年来，极端风场短时预测因其对工程安全的重要性，已成为国际风工程领域的研究热点与学术前沿。在极端风场来临前，精准预测原位风速对于工程结构安全预警与应急防护具有重要意义。传统数值天气预报方法是极端风场预测的有效手段，但由于其空间分辨率不足、计算资源消耗大，难以实现工程结构原位风速的实时预测。随着人工智能技术的快速发展，机器学习为解决上述难题提供了新思路，在极端风场短时预测中的应用日趋广泛，且展露出广阔的应用前景。据此，本文对基于机器学习的极端风场短时预测研究进展进行了综述。首先，回顾了时间序列模型、机器学习模型和混合模型在风场预测中的应用原理及其特点；其次，从良态强风、台风、雷暴风3种频发强风的角度，分类论述了常采用的极端风场短时预测方法，并对其优缺点进行了归纳总结。最后，针对极端风场短时预测的研究现状与挑战，阐述了对该领域未来潜在研究方向的思考。

Abstract

In recent years

short-term extreme wind fields prediction has become a research hotspot and academic frontier in the area of international wind engineering due to its vital role in structural safety. Accurate prediction of in-situ wind speed before the arrival of extreme wind fields is of great significance for the early warning of engineering structures safety and emergency protection. The traditional numerical weather prediction method is effective for extreme wind field prediction. However

due to insufficient spatial resolution and high consumption of computing resources

it is difficult to provide a real-time prediction of in-situ wind speed for engineering structures. With the rapid development of artificial intelligence technology

machine learning offers new ideas for solving the problems mentioned above. It is increasingly widely applied in short-term extreme wind fields prediction

showing broad application prospects. In this regard

this paper provides a comprehensive review of recent progress in the short-term extreme wind fields prediction using machine learning-based approaches. Firstly

the application principles and characteristics of time series models

machine learning models

and hybrid models in wind field prediction are reviewed. Subsequently

we classify and evaluate prevalent methods for short-term extreme wind field prediction

focusing on three predominant wind types: regular strong winds

typhoons

and thunderstorm winds. Their advantages and limitations are summarized. Finally

considering current research gaps and challenges in short-term prediction of extreme wind fields

potential future directions are proposed.

关键词

Keywords

references

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