【个人简介】
刘隽斐，男，讲师，1995年生，博士研究生。主要研究方向：冰冻雨雪灾害、自然灾害模拟与建模、灾害机制发现、风险评估、城市韧性等。研究方法包括深度学习、复杂网络等。目前发表SCI论文9篇，以第一作者发表5篇。兼职担任Scientific Data、Artificial Intelligence Review、Environmental Development、npj Natural Hazards、Natural Hazards等期刊审稿人。邮箱：junfeiliu@cuit.edu.cn
【学习与工作经历】
2026.7–至今，成都信息工程大学，讲师
2021.9–2026.6，北京师范大学地理科学学部，自然灾害学专业，理学博士
2018.9–2019.11，英国克兰菲尔德大学，地理信息管理专业，理学硕士
2014.9–2018.6，中国农业大学，环境工程，工学学士
【论著专利】
论文
1.Liu J, Wang M, Liu K. Assessment and Mapping Extreme Ice Accretion Damage on Electricity Infrastructure Due to Freezing Rain in China[J]. International Journal of Disaster Risk Science, 2026.https://doi.org/10.1007/s13753-026-00730-0.（1区）
2.Liu J, Wang M, Liu K. Interpretable Deep Reinforcement Learning Optimizes Emergency Response for China's Freezing Rain-Impacted Roads[J]. Transportation Research Part D: Transport and Environment, Volume 150, 2026,105076, ISSN 1361-9209.（1区TOP）
3.Liu J, Wang M, Liu K, Li K, Zhang J. Complex Network Uncovers Key Propagation Patterns of Extreme Freezing Rain Events in China[J]. Climate Dynamics 64, 58 (2026).（2区）
4.Liu J, Liu K, Wang M. A 6-hourly 0.1° resolution freezing rain dataset of China during 2000–2019 based on deep kernel learning[J]. Scientific Data, 2025, 12(1): 240.（Nature旗下刊物，2区）
5.Liu J, Liu K, Wang M. A Residual Neural Network Integrated with a Hydrological Model for Global Flood Susceptibility Mapping based on Remote Sensing Datasets[J]. Remote Sensing, 2023, 15(9): 2447.（2区TOP）
6.Zhang J, Liu K, Sui Y, Li K, Qin L,Liu J, Wang M, Marwan N, Oscillation-induced synchronization hubs in global hydrological extremes, Journal of Hydrology, 674, 2026, 135550, https://doi.org/10.1016/j.jhydrol.2026.135550.
7.ZhuangL, WangM,LiuK,TangLC, WuJ,XuD,LiuJ, ZhangJ, YangJ, RenY& XuD.Assessing and Optimizing Urban Dynamic Resilience to Extreme Rainfall from Shock to Recovery. International Journal of Disaster Risk Science,17, 128–147 (2026).https://doi.org/10.1007/s13753-025-00683-w
8.LiB,LiuK, WangM, ZhuW, JiangZ, QiaoN, YanY,LiuJ, ZhaoJ, LiC. Spatiotemporal warning of rainfall-induced railway geohazards in China, Engineering Geology, 355, 2025, 108234, ISSN 0013-7952, https://doi.org/10.1016/j.enggeo.2025.108234.
9.LiuK, ZhangJ,LiuJ, WangM, YueQ.Projection of land susceptibility to subsidence hazard in China using an interpretable CNN deep learning model[J].Science of the Total Environment, 2024,913, 169502.
