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2026/08/10 22:37 |
Jae******Park |
I am a graduate researcher at Civil Engineering, Hanyang University,
Republic of Korea, working on video-based early recognition of debris-flow
and landslide hazards for real-time monitoring systems.
Publicly available video footage of actual debris-flow events is extremely
scarce, which makes it difficult to validate video-based recognition methods
under real field conditions. The Jiangjia Ravine video dataset provides
high-resolution recordings of genuine debris-flow events captured by fixed
channel-side cameras, and is therefore essential for evaluating the temporal
behaviour of our method (surge arrival, front propagation, and flow-stage
transitions) against real observations rather than laboratory experiments.
The data will be used strictly for non-commercial academic research: model
development, validation, and result verification. The data will not be
redistributed to any third party. Results will be published in a peer-reviewed
journal, where I will cite the dataset following the required format and
acknowledge the Dongchuan Debris Flow Observation and Research Station (DDFORS),
Chinese Academy of Sciences, and the National Cryosphere Desert Data Center.
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| 5 |
2026/07/23 23:33 |
石*婷 |
Evolution Characteristics and Risk Prediction of Landslide–Debris Flow Hazard Chains under Climate Change: A Case Study of the Jiangjia Gully Catchment, Dongchuan, Yunnan
Abstract:Landslides and debris flows in mountainous regions often develop as cascading hazards controlled by the combined effects of rainfall, topography, sediment supply, soil moisture, and channel dynamics. This study focuses on the Jiangjia Gully catchment in Dongchuan, Yunnan Province, and integrates historical debris-flow records, rheological parameters, channel cross-section measurements, soil moisture and temperature observations, streamflow data, and meteorological monitoring data. The objectives are to investigate the coupled relationships among landslide-derived sediment supply, rainfall infiltration, and channel erosion and deposition, and to identify the key factors governing the initiation and evolution of landslide–debris flow hazard chains. A multi-source data-driven prediction model will then be developed to estimate hazard probability and determine critical triggering thresholds. The results are expected to improve the understanding of landslide–debris flow interactions and provide scientific support for dynamic monitoring, early warning, and risk mitigation in mountainous areas.
期刊论文
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本作品采用
CC BY 4.0 （知识共享 署名 4.0 国际许可协议）进行许可。
CSTR:11738.11.ncdc.ddfors.db7468.2026
10.12072/ncdc.ddfors.db7468.2026
