使用Rusa台风的深度学习算法进行建筑损失评估.
Ji-Myong Kim1, Junseo Bae2, Manik Das Adhikari3
1Department of Architectural Engineering, Mokpo National University, Mokpo, 58554, South Korea.
Heliyon
|January 1, 2024
概括
这项研究引入了一个深度学习模型来预测台风损害,为灾害管理提供了一种新的方法. 该模型旨在改善气候相关灾难的风险评估和应对计划.
科学领域:
- 气候科学 气候科学
- 人工智能的人工智能
- 灾害管理 灾害管理
背景情况:
- 气候危机,包括极端天气和自然灾害,对全球造成重大破坏.
- 私营部门和政府目前为减轻与气候有关的损害所做的努力不足以满足市场需求.
- 台风造成的损害构成重大威胁,需要先进的预测方法.
研究的目的:
- 开发和验证用于预测台风损害的深度学习算法.
- 为加强灾害管理和应对气候危机的弹性提供一个新的框架.
- 为政府机构,设施经理和保险公司提供预测和减轻台风影响的工具.
主要方法:
- 深度神经网络 (DNN) 模型的开发.
- 模型训练使用台风鲁萨损害的历史数据.
- 使用平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 的评估,与传统多回归模型进行比较分析.
主要成果:
- DNN模型在预测台风损害方面表现出了准确性和弹性.
- 验证指标 (MAE,RMSE) 证实了该模型的性能.
- 对比分析表明,拟议的框架优于传统方法.
结论:
- 提出的深度学习方法为预测台风损害提供了一种新且有效的方法.
- 这一框架可以显著帮助灾害管理,风险评估和保险规划.
- 实施这种模型可以帮助减少台风的破坏,并增强对气候变化影响的抵御力.
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