A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

S Liu, L Wang, W Zhang, Y He, S Pijush - Geological Journal, 2023 - Wiley Online Library
Landslide susceptibility mapping (LSM) has been widely used as an important reference for
development and construction planning to mitigate the potential social‐eco impact caused …

[HTML][HTML] BIM–GIS integrated utilization in urban disaster management: the contributions, challenges, and future directions

Y Cao, C Xu, NM Aziz, SN Kamaruzzaman - Remote Sensing, 2023 - mdpi.com
In the 21st Century, disasters have severe negative impacts on cities worldwide. Given the
significant casualties and property damage caused by disasters, it is necessary for disaster …

[HTML][HTML] 基于优化负样本采样策略的梯度提升决策树与随机森林的汶川同震滑坡易发性评价

郭衍昊, 窦杰, 向子林, 马豪, 董傲男, 罗万祺 - 地质科技通报, 2024 - dzkjqb.cug.edu.cn
强震诱发的滑坡具有数量多, 分布广, 规模大等特点, 严重威胁人民生命财产安全.
滑坡易发性评价能够快速预测灾害空间分布, 对于减轻震后灾害的危险性具有重要意义 …

Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin, North-Western Himalayas

SK Gupta, DP Shukla - Landslides, 2023 - Springer
Abstract Machine learning methods require a vast amount of data to train a model. The data
necessary for landslide susceptibility mapping is a collection of landslide causative factors …

[HTML][HTML] Global dynamic rainfall-induced landslide susceptibility mapping using machine learning

B Li, K Liu, M Wang, Q He, Z Jiang, W Zhu, N Qiao - Remote Sensing, 2022 - mdpi.com
Precipitation is the main factor that triggers landslides. Rainfall-induced landslide
susceptibility mapping (LSM) is crucial for disaster prevention and disaster losses mitigation …

Unraveling the evolution of landslide susceptibility: a systematic review of 30-years of strategic themes and trends

A Dong, J Dou, Y Fu, R Zhang, K Xing - Geocarto International, 2023 - Taylor & Francis
Landslide susceptibility mapping (LSM) research is vital for averting and mitigating regional
landslide disasters. Nevertheless, there has been a lack of systematic analysis regarding …

Performance comparison of landslide susceptibility mapping under multiple machine-learning based models considering InSAR deformation: a case study of the …

J Yao, X Yao, Z Zhao, X Liu - Geomatics, Natural Hazards and Risk, 2023 - Taylor & Francis
Landslide susceptibility mapping (LSM) comprehensively evaluates the spatial probability of
landslide occurrence by using different environmental factors. However, most of the …

[HTML][HTML] 甘肃积石山县MS 6.2地震同震地质灾害发育特征与易发性评价

刘帅, 何斌, 王涛, 刘甲美, 曹佳文, 王浩杰… - 地质力学 …, 2024 - journal.geomech.ac.cn
2023 年12 月18 日, 甘肃积石山县发生MS 6.2 地震, 诱发的同震地质灾害严重威胁到人民生命
和财产安全, 因此及时总结分析同震地质灾害发育规律并进行县域易发性评价 …

[PDF][PDF] 基于知识图谱的滑坡易发性评价文献综述及研究进展

郭飞, 赖鹏, 黄发明, 刘磊磊, 王秀娟, 何政宇 - 地球科学, 2024 - earth-science.net
滑坡易发性评价是滑坡风险评估的基础和核心内容, 开展滑坡易发性文献计量分析可以定量化地
分析其研究进展及发展趋势, 为国内开展地灾风险评估工作提供参考. 利用Web of Science …

[HTML][HTML] Utilizing hybrid machine learning and soft computing techniques for landslide susceptibility mapping in a Drainage Basin

Y Mao, Y Li, F Teng, AKS Sabonchi, M Azarafza… - Water, 2024 - mdpi.com
The hydrological system of thebasin of Lake Urmia is complex, deriving its supply from a
network comprising 13 perennial rivers, along withnumerous small springs and direct …