Articles | Volume 18, issue 6
https://doi.org/10.5194/nhess-18-1717-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-18-1717-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jun 2018
Research article |
| 20 Jun 2018
| 20 Jun 2018
Adopting the I3–R24 rainfall index and landslide susceptibility for the establishment of an early warning model for rainfall-induced shallow landslides
Lun-Wei Wei
CORRESPONDING AUTHOR
Department of Geosciences, National Taiwan University, Taipei City, Taiwan
Disaster Prevention Technology Research Center, Sinotech
Engineering Consultants, INC., Taipei City, Taiwan
Chuen-Ming Huang
Institute of Applied Geology, National Central University, Taoyuan City, Taiwan
Disaster Prevention Technology Research Center, Sinotech
Engineering Consultants, INC., Taipei City, Taiwan
Hongey Chen
Department of Geosciences, National Taiwan University, Taipei City, Taiwan
National Science and Technology Center for Disaster Reduction, New Taipei City,
Taiwan
Chyi-Tyi Lee
Institute of Applied Geology, National Central University, Taoyuan City, Taiwan
Chun-Chi Chi
Central Geological Survey, MOEA, New Taipei City, Taiwan
Chen-Lung Chiu
Central Geological Survey, MOEA, New Taipei City, Taiwan
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25 citations as recorded by crossref.
- Rainfall threshold for initiating effective stress decrease and failure in weathered tephra slopes M. Kluger et al. 10.1007/s10346-019-01289-2
- Application of hydrological modelling for temporal prediction of rainfall-induced shallow landslides N. Bezak et al. 10.1007/s10346-019-01169-9
- Machine learning in earthquake- and typhoon-triggered landslide susceptibility mapping and critical factor identification M. Ali et al. 10.1007/s12665-021-09510-z
- Research on a Regional Landslide Early-Warning Model Based on Machine Learning—A Case Study of Fujian Province, China Y. Liu et al. 10.3390/f13122182
- GIS-based landslide susceptibility mapping using ensemble methods for Fengjie County in the Three Gorges Reservoir Region, China W. Gong et al. 10.1007/s13762-021-03572-z
- Improved Method of Defining Rainfall Intensity and Duration Thresholds for Shallow Landslides Based on TRIGRS S. Zhang et al. 10.3390/w14040524
- A shallow slide prediction model combining rainfall threshold warnings and shallow slide susceptibility in Busan, Korea A. Pradhan et al. 10.1007/s10346-018-1112-z
- Application of High-Resolution Radar Rain Data to the Predictive Analysis of Landslide Susceptibility under Climate Change in the Laonong Watershed, Taiwan C. Tseng et al. 10.3390/rs12233855
- Geographical landslide early warning systems F. Guzzetti et al. 10.1016/j.earscirev.2019.102973
- Analyzing trend and forecasting of temperature and rainfall in Shimla district of Himachal Pradesh, India using non-parametric and bagging REPTree machine learning approaches A. Sharma et al. 10.1016/j.jastp.2024.106352
- An hourly shallow landslide warning model developed by combining automatic landslide spatial susceptibility and temporal rainfall threshold predictions Y. Cao et al. 10.1007/s11629-022-7370-1
- Construction of landslide warning by combining rainfall threshold and landslide susceptibility in the gully region of the Loess Plateau: A case of Lanzhou City, China H. Shu et al. 10.1016/j.jhydrol.2024.132148
- Influence of below-threshold rainfall on landslide occurrence based on Japanese cases S. Kaihara et al. 10.1007/s11069-022-05639-7
- Landslides triggered in weathered crystalline rocks of moderate latitudes: A case study in Mediterranean environment (The Maures Massif, France) Y. Thiery et al. 10.1016/j.enggeo.2018.12.002
- Rainfall early warning threshold and its spatial distribution of rainfall-induced landslides in China H. Dou et al. 10.1016/j.rockmb.2023.100056
- Hong Kong’s landslip warning system—40 years of progress V. Kong et al. 10.1007/s10346-020-01379-6
- A comparative study of regional rainfall-induced landslide early warning models based on RF、CNN and MLP algorithms Y. Liu et al. 10.3389/feart.2024.1419421
- Influence of soil mechanical and hydraulic parameters on the definition of rainfall intensity and duration thresholds based on Transient rainfall infiltration and grid-based regional slope-stability model (TRIGRS) S. Zhang et al. 10.3389/feart.2022.971655
- Reviews and Syntheses: Promoting the Advancement of Hillslope Hydrology and Stability in Taiwan from the Perspective of Critical Zone Science Y. Yang et al. 10.3390/w15061234
- Space-time modeling of landslide size by combining static, dynamic, and unobserved spatiotemporal factors Z. Fang et al. 10.1016/j.catena.2024.107989
- Preface: Landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception S. Segoni et al. 10.5194/nhess-18-3179-2018
- Determination of Empirical Rainfall Thresholds for Shallow Landslides in Slovenia Using an Automatic Tool G. Jordanova et al. 10.3390/w12051449
- Rainfall infiltration and slope stability of alpine colluvial terraces subject to storms (NE Italy) P. Paronuzzi & A. Bolla 10.1016/j.enggeo.2023.107199
- Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale M. Bordoni et al. 10.1007/s10346-020-01592-3
- Hydrological control of large hurricane-induced lahars: evidence from rainfall-runoff modeling, seismic and video monitoring L. Capra et al. 10.5194/nhess-18-781-2018
24 citations as recorded by crossref.
- Rainfall threshold for initiating effective stress decrease and failure in weathered tephra slopes M. Kluger et al. 10.1007/s10346-019-01289-2
- Application of hydrological modelling for temporal prediction of rainfall-induced shallow landslides N. Bezak et al. 10.1007/s10346-019-01169-9
- Machine learning in earthquake- and typhoon-triggered landslide susceptibility mapping and critical factor identification M. Ali et al. 10.1007/s12665-021-09510-z
- Research on a Regional Landslide Early-Warning Model Based on Machine Learning—A Case Study of Fujian Province, China Y. Liu et al. 10.3390/f13122182
- GIS-based landslide susceptibility mapping using ensemble methods for Fengjie County in the Three Gorges Reservoir Region, China W. Gong et al. 10.1007/s13762-021-03572-z
- Improved Method of Defining Rainfall Intensity and Duration Thresholds for Shallow Landslides Based on TRIGRS S. Zhang et al. 10.3390/w14040524
- A shallow slide prediction model combining rainfall threshold warnings and shallow slide susceptibility in Busan, Korea A. Pradhan et al. 10.1007/s10346-018-1112-z
- Application of High-Resolution Radar Rain Data to the Predictive Analysis of Landslide Susceptibility under Climate Change in the Laonong Watershed, Taiwan C. Tseng et al. 10.3390/rs12233855
- Geographical landslide early warning systems F. Guzzetti et al. 10.1016/j.earscirev.2019.102973
- Analyzing trend and forecasting of temperature and rainfall in Shimla district of Himachal Pradesh, India using non-parametric and bagging REPTree machine learning approaches A. Sharma et al. 10.1016/j.jastp.2024.106352
- An hourly shallow landslide warning model developed by combining automatic landslide spatial susceptibility and temporal rainfall threshold predictions Y. Cao et al. 10.1007/s11629-022-7370-1
- Construction of landslide warning by combining rainfall threshold and landslide susceptibility in the gully region of the Loess Plateau: A case of Lanzhou City, China H. Shu et al. 10.1016/j.jhydrol.2024.132148
- Influence of below-threshold rainfall on landslide occurrence based on Japanese cases S. Kaihara et al. 10.1007/s11069-022-05639-7
- Landslides triggered in weathered crystalline rocks of moderate latitudes: A case study in Mediterranean environment (The Maures Massif, France) Y. Thiery et al. 10.1016/j.enggeo.2018.12.002
- Rainfall early warning threshold and its spatial distribution of rainfall-induced landslides in China H. Dou et al. 10.1016/j.rockmb.2023.100056
- Hong Kong’s landslip warning system—40 years of progress V. Kong et al. 10.1007/s10346-020-01379-6
- A comparative study of regional rainfall-induced landslide early warning models based on RF、CNN and MLP algorithms Y. Liu et al. 10.3389/feart.2024.1419421
- Influence of soil mechanical and hydraulic parameters on the definition of rainfall intensity and duration thresholds based on Transient rainfall infiltration and grid-based regional slope-stability model (TRIGRS) S. Zhang et al. 10.3389/feart.2022.971655
- Reviews and Syntheses: Promoting the Advancement of Hillslope Hydrology and Stability in Taiwan from the Perspective of Critical Zone Science Y. Yang et al. 10.3390/w15061234
- Space-time modeling of landslide size by combining static, dynamic, and unobserved spatiotemporal factors Z. Fang et al. 10.1016/j.catena.2024.107989
- Preface: Landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception S. Segoni et al. 10.5194/nhess-18-3179-2018
- Determination of Empirical Rainfall Thresholds for Shallow Landslides in Slovenia Using an Automatic Tool G. Jordanova et al. 10.3390/w12051449
- Rainfall infiltration and slope stability of alpine colluvial terraces subject to storms (NE Italy) P. Paronuzzi & A. Bolla 10.1016/j.enggeo.2023.107199
- Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale M. Bordoni et al. 10.1007/s10346-020-01592-3
Latest update: 23 Nov 2024
Short summary
The difference in susceptibility might lead to a difference in warning threshold for rainfall-induced landslides. Here we divided slope units into three susceptibility levels and established their thresholds separately. It was found that the threshold values gradually increased as the susceptibility decreased for the same alert level. This showed that classifying susceptibility and establishing thresholds separately might provide refined thresholds for disaster prevention.
The difference in susceptibility might lead to a difference in warning threshold for...
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