Leveraging world's largest offshore observation networks and physics-based model integration with machine learning for real-time tsunami and storm surge forecasting along the Pacific coasts of Japan

利用世界上最大的海上观测网络以及基于物理的模型与机器学习的集成，对日本太平洋沿岸的实时海啸和风暴潮进行预测

• 批准号: 22K14459
• 负责人: MULIA IYAN
• 金额: $ 2.33万
• 依托单位: Institute of Physical and Chemical Research
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Early-Career Scientists
• 财政年份: 2022
• 资助国家: 日本
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22K14459/
• 关键词: Disaster mitigation; Machine learning; Tsunami; Storm surge; Real-time forecasting; Natural hazard; Prediction

The first part of the proposed research plan was implemented in the first year. A method to forecast tsunami inundation in real-time using machine learning and data observed at the S-net observing systems has been developed and successfully applied to several past tsunami events. The main advantage of this new method is related to the computational speed, which is crucial in the tsunami early warning system. In comparison, the conventional methods through a numerical simulation take approximately 30 minutes to predict tsunami inundation in the specified study area. In contrast, with comparable accuracy, the proposed method requires less than a second. The results have been published in a scientific journal, including domestic and international conferences. In the second part of the proposed research, similar methods but with improvements relative to the first part will be applied for storm surge forecasting.

拟定的研究计划的第一部分在第一年实施。一种利用机器学习和S-net观测系统观测数据实时预测海啸淹没的方法已经开发出来，并成功地应用于过去的几次海啸事件。这种新方法的主要优点是计算速度快，这在海啸预警系统中是至关重要的。相比之下，传统的方法通过数值模拟来预测指定研究区域的海啸淹没大约需要30分钟。相比之下，在精度相当的情况下，所提出的方法只需要不到一秒的时间。研究结果已发表在包括国内和国际会议在内的科学期刊上。在本研究的第二部分中，将采用与第一部分相似但有所改进的方法进行风暴潮预报。

项目成果

Sensitivity of Tsunami Data to the Up-Dip Extent of the July 2021 Mw 8.2 Alaska Earthquake

• DOI: 10.1785/0220210359
• 发表时间: 2022-05
• 期刊: Seismological Research Letters
• 影响因子: 3.3
• 作者: I. Mulia;A. Gusman;M. Heidarzadeh;K. Satake

Asia Oceania Geosciences Society

亚洲大洋洲地球科学会

• 发表时间: 2022

The use of S-net data for tsunami inundation forecasting using machine learning

使用 S-net 数据通过机器学习进行海啸淹没预报

• 发表时间: 2022
• 作者: Mulia Iyan E.;Ueda Naonori;Miyoshi Takemasa;Gusman Aditya Riadi;Satake Kenji
• 通讯作者: Satake Kenji

GNS Science(ニュージーランド)

GNS科学（新西兰）

University of Bath(英国)

巴斯大学（英国）