A federated learning based approach for predicting landslide displacement considering data security

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成果归属作者:

梅钢

成果归属机构:

工程技术学院

作者

Yang, Yuting ; Lu, Yue ; Mei, Gang

单位

China Univ Geosci Beijing, Sch Engn & Technol, Xueyuan Rd 29, Beijing 100083, Peoples R China

关键词

3 GORGES RESERVOIR; SHUPING LANDSLIDE; IMPOUNDMENT; MOVEMENT; AREA

摘要

Homeland security is an important concern in contemporary society. National mega strategic engi-neering areas and other key regions, characterized by the presence of high mountains and valleys, are prone to various geological hazards, including landslides. Therefore, the timely geological hazard prediction and forecasting are required, and local data security protection is also crucial. To address the aforementioned problems, a landslide displacement prediction method based on federated learning which can protect data security is proposed in this paper and validated in the Three Gorges Project area. The essential idea is to employ the federated learning approach to enable the joint training of deep learning models for landslide displacement prediction without exchanging data. The proposed method (1) trains each landslide displacement prediction model locally without data exchange, ensuring geospatial data security, and (2) improves the accuracy of landslide displacement prediction in most cases, protecting people's lives and properties. The proposed method has the potential to improve the prediction and forecasting of geological hazards in other key areas, thereby protecting people's lives and properties while ensuring national homeland security. & COPY; 2023 Elsevier B.V. All rights reserved.

基金

National Natural Science Foundation of China [42277161]

语种

英文

来源

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,2023():184-199.

出版日期

2023-12

提交日期

2023-09-06

引用参考

Yang, Yuting; Lu, Yue; Mei, Gang. A federated learning based approach for predicting landslide displacement considering data security[J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,2023():184-199.

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