Landslide Susceptibility Assessment via Imbalanced Data Augmentation with Tabular Variational Autoencoder and Quality–Diversity Post-Selection

Landslides are among the most common geological hazards in mountainous regions, posing significant threats to resident safety and infrastructure stability. Due to the complexity of terrain and the difficulty of field surveys, landslide samples in these areas often suffer from class imbalance, which...

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Vydané v:	Applied sciences Ročník 15; číslo 22; s. 11965
Hlavní autori:	Xu, Zhengyang, Wang, Shitai, Yin, Min, Zhang, Xiaoyu, Lu, Zengyang, Yu, Songchao, Huang, Junjun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.11.2025
Predmet:	Accuracy Algorithms China class imbalance data augmentation Datasets Disasters Earthquakes Emergency communications systems Emergency preparedness Geology Hydrology landslide susceptibility Landslides Landslides & mudslides Lithology Precipitation Puerto Rico quality–diversity Rain and rainfall Risk assessment Risk management Surveys Tabular Variational Autoencoder Topography Variables Vegetation Puerto Rico China
ISSN:	2076-3417, 2076-3417
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