利用LSTM和XGBoost算法进行风暴预测
Ayyoub Frifra1,2, Mohamed Maanan3, Mehdi Maanan2
1UMR 6554 CNRS LETG-Nantes Laboratory, Institute of Geography and Planning, Nantes University, 44312, Nantes, France.
Scientific reports
|May 18, 2024
概括
预测风暴很困难,但一项新的研究使用长期短期记忆 (LSTM) 和极端梯度提升 (XGBoost) 来预测法国西部风暴特征和发生情况,显示出有希望的结果.
科学领域:
- 气象学和气候学
- 数据科学和人工智能数据科学和人工智能
- 海洋学 海洋学 海洋学
背景情况:
- 风暴对人类生命和基础设施构成重大风险.
- 精确的风暴预测受到这些事件的不频繁性质的阻碍.
- 需要先进的计算方法来改善风暴预报.
研究的目的:
- 开发和评估一种新的深度学习和机器学习方法,用于预测风暴特征和发生情况.
- 评估长短期记忆 (LSTM) 和极端梯度增强 (XGBoost) 模型在风暴预测中的有效性.
- 将这些模型应用于法国西部的风暴数据.
主要方法:
- 利用浮标数据和跨越1996-2020年的风暴数据库的数据组合.
- 经过训练和验证的LSTM和XGBoost模型使用1996年1月至2015年12月的数据.
- 从2016年1月到2020年12月,应用训练模型来预测风暴特征和发生情况.
主要成果:
- 该LSTM模型在预测温度和压力方面表现出高精度,但面临极端波浪高度和风速的挑战.
- XGBoost模型在预测风暴发生方面表现出色.
- 结合的方法为增强风暴预测能力提供了一个强大的框架.
结论:
- 深度学习和机器学习模型,特别是LSTM和XGBoost,可以有效地帮助预测风暴.
- 开发的方法表明,有可能减少风暴对人类人口和基础设施的影响.
- 进一步细化可能会改善极端风暴参数的预测.
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