Articles | Volume 19, issue 1
https://doi.org/10.5194/nhess-19-201-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-19-201-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jan 2019
Research article |
| 25 Jan 2019
| 25 Jan 2019
On the nexus between landslide susceptibility and transport infrastructure – an agent-based approach
Matthias Schlögl
CORRESPONDING AUTHOR
Transportation Infrastructure Technologies, Austrian Institute of Technology (AIT), Vienna, Austria
Institute of Applied Statistics and Computing, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
Institute of Mountain Risk Engineering, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
Staff Unit Earth Observation, Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Vienna, Austria
Gerald Richter
Dynamic Transportation Systems, Austrian Institute of Technology (AIT), Vienna, Austria
Michael Avian
Staff Unit Earth Observation, Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Vienna, Austria
Thomas Thaler
Institute of Mountain Risk Engineering, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
Gerhard Heiss
Environmental Impact Assessment, Austrian Institute of Technology (AIT), Vienna, Austria
Gernot Lenz
Dynamic Transportation Systems, Austrian Institute of Technology (AIT), Vienna, Austria
Sven Fuchs
Institute of Mountain Risk Engineering, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
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Cited
34 citations as recorded by crossref.
- Vulnerability patterns of road network to extreme floods based on accessibility measures T. Papilloud & M. Keiler 10.1016/j.trd.2021.103045
- Preface: Landslide–transport network interactions F. Taylor et al. 10.5194/nhess-20-2585-2020
- Vulnerability analysis in complex networks under a flood risk reduction point of view L. Santos et al. 10.3389/fphy.2023.1064122
- A novel landslide susceptibility optimization framework to assess landslide occurrence probability at the regional scale for environmental management X. Sun et al. 10.1016/j.jenvman.2022.116108
- Spatial agents for geological surface modelling E. de Kemp 10.5194/gmd-14-6661-2021
- Landslide Risks to Bridges in Valleys in North Carolina S. Lin et al. 10.3390/geohazards5010015
- Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping B. Pham et al. 10.1016/j.catena.2020.104805
- Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization X. Zhou et al. 10.1016/j.gsf.2021.101211
- Data use and data needs in critical infrastructure risk analysis A. Larsson & C. Große 10.1080/13669877.2023.2181858
- Recent advances in vulnerability assessment for the built environment exposed to torrential hazards: Challenges and the way forward S. Fuchs et al. 10.1016/j.jhydrol.2019.05.067
- National and regional-scale landslide indicators and indexes: Applications in Italy M. Donnini et al. 10.1515/geo-2022-0375
- Landslides along the Lago Maggiore western coast (northern Italy): intense rainfall as trigger or concomitant cause? M. Ciampittiello et al. 10.1007/s11069-021-04626-8
- Total probabilistic measure for the potential risk of regional roads exposed to landslides Q. Liu et al. 10.1016/j.ress.2022.108822
- Modelling landslides in the Lesser Himalaya region using geospatial and numerical simulation techniques M. Islam & S. Chattoraj 10.1007/s12517-023-11541-8
- Physical vulnerability to dynamic flooding: Vulnerability curves and vulnerability indices M. Papathoma-Köhle et al. 10.1016/j.jhydrol.2022.127501
- Developing a probability-based technique to improve the measurement of landslide vulnerability on regional roads Q. Liu et al. 10.1016/j.ress.2023.109918
- Short communication: A model to predict flood loss in mountain areas S. Fuchs et al. 10.1016/j.envsoft.2019.03.026
- Identification and evaluation of flood-avoidance routes in Tucson, Arizona A. Coles 10.1016/j.ijdrr.2020.101597
- Natural hazard impacts on transport infrastructure in Russia E. Petrova 10.5194/nhess-20-1969-2020
- Estimation of traffic flow changes using networks in networks approaches J. Hackl & B. Adey 10.1007/s41109-019-0139-y
- Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning H. He et al. 10.3390/su16093828
- The (f)utility to account for pre-failure topography in data-driven landslide susceptibility modelling S. Steger et al. 10.1016/j.geomorph.2020.107041
- Multi-hazard risk assessment of rail infrastructure in India under local vulnerabilities towards adaptive pathways for disaster resilient infrastructure planning D. Joshi et al. 10.1016/j.pdisas.2023.100308
- Analysis of historical data for a better understanding of post‐construction landslides at an artificial waterway A. Wohlers & B. Damm 10.1002/esp.5028
- Landslide Susceptibility Mapping in a Mountainous Area Using Machine Learning Algorithms H. Shahabi et al. 10.3390/rs15123112
- Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area B. Ghasemian et al. 10.3389/fenvs.2022.897254
- Sample size effects on landslide susceptibility models: A comparative study of heuristic, statistical, machine learning, deep learning and ensemble learning models with SHAP analysis S. Yang et al. 10.1016/j.cageo.2024.105723
- Rockfall Vulnerability of a Rural Road Network—A Methodological Approach in the Harz Mountains, Germany A. Wohlers & B. Damm 10.3390/geosciences12040170
- Multi-hazard risk assessment for roads: probabilistic versus deterministic approaches S. Oberndorfer et al. 10.5194/nhess-20-3135-2020
- Quantifying climate risks to infrastructure systems: A comparative review of developments across infrastructure sectors J. Verschuur et al. 10.1371/journal.pclm.0000331
- Development of multiclass alternating decision trees based models for landslide susceptibility mapping B. Pham et al. 10.1016/j.pce.2022.103235
- Rainfall-induced transportation embankment failure: A review X. Linrong et al. 10.1515/geo-2022-0558
- A machine learning approach in spatial predicting of landslides and flash flood susceptible zones for a road network H. Ha et al. 10.1007/s40808-022-01384-9
- Quantitative assessment of the impact of earthquake-induced geohazards on natural landscapes in Jiuzhaigou Valley X. Hu et al. 10.1007/s11629-018-5240-7
33 citations as recorded by crossref.
- Vulnerability patterns of road network to extreme floods based on accessibility measures T. Papilloud & M. Keiler 10.1016/j.trd.2021.103045
- Preface: Landslide–transport network interactions F. Taylor et al. 10.5194/nhess-20-2585-2020
- Vulnerability analysis in complex networks under a flood risk reduction point of view L. Santos et al. 10.3389/fphy.2023.1064122
- A novel landslide susceptibility optimization framework to assess landslide occurrence probability at the regional scale for environmental management X. Sun et al. 10.1016/j.jenvman.2022.116108
- Spatial agents for geological surface modelling E. de Kemp 10.5194/gmd-14-6661-2021
- Landslide Risks to Bridges in Valleys in North Carolina S. Lin et al. 10.3390/geohazards5010015
- Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping B. Pham et al. 10.1016/j.catena.2020.104805
- Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization X. Zhou et al. 10.1016/j.gsf.2021.101211
- Data use and data needs in critical infrastructure risk analysis A. Larsson & C. Große 10.1080/13669877.2023.2181858
- Recent advances in vulnerability assessment for the built environment exposed to torrential hazards: Challenges and the way forward S. Fuchs et al. 10.1016/j.jhydrol.2019.05.067
- National and regional-scale landslide indicators and indexes: Applications in Italy M. Donnini et al. 10.1515/geo-2022-0375
- Landslides along the Lago Maggiore western coast (northern Italy): intense rainfall as trigger or concomitant cause? M. Ciampittiello et al. 10.1007/s11069-021-04626-8
- Total probabilistic measure for the potential risk of regional roads exposed to landslides Q. Liu et al. 10.1016/j.ress.2022.108822
- Modelling landslides in the Lesser Himalaya region using geospatial and numerical simulation techniques M. Islam & S. Chattoraj 10.1007/s12517-023-11541-8
- Physical vulnerability to dynamic flooding: Vulnerability curves and vulnerability indices M. Papathoma-Köhle et al. 10.1016/j.jhydrol.2022.127501
- Developing a probability-based technique to improve the measurement of landslide vulnerability on regional roads Q. Liu et al. 10.1016/j.ress.2023.109918
- Short communication: A model to predict flood loss in mountain areas S. Fuchs et al. 10.1016/j.envsoft.2019.03.026
- Identification and evaluation of flood-avoidance routes in Tucson, Arizona A. Coles 10.1016/j.ijdrr.2020.101597
- Natural hazard impacts on transport infrastructure in Russia E. Petrova 10.5194/nhess-20-1969-2020
- Estimation of traffic flow changes using networks in networks approaches J. Hackl & B. Adey 10.1007/s41109-019-0139-y
- Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning H. He et al. 10.3390/su16093828
- The (f)utility to account for pre-failure topography in data-driven landslide susceptibility modelling S. Steger et al. 10.1016/j.geomorph.2020.107041
- Multi-hazard risk assessment of rail infrastructure in India under local vulnerabilities towards adaptive pathways for disaster resilient infrastructure planning D. Joshi et al. 10.1016/j.pdisas.2023.100308
- Analysis of historical data for a better understanding of post‐construction landslides at an artificial waterway A. Wohlers & B. Damm 10.1002/esp.5028
- Landslide Susceptibility Mapping in a Mountainous Area Using Machine Learning Algorithms H. Shahabi et al. 10.3390/rs15123112
- Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area B. Ghasemian et al. 10.3389/fenvs.2022.897254
- Sample size effects on landslide susceptibility models: A comparative study of heuristic, statistical, machine learning, deep learning and ensemble learning models with SHAP analysis S. Yang et al. 10.1016/j.cageo.2024.105723
- Rockfall Vulnerability of a Rural Road Network—A Methodological Approach in the Harz Mountains, Germany A. Wohlers & B. Damm 10.3390/geosciences12040170
- Multi-hazard risk assessment for roads: probabilistic versus deterministic approaches S. Oberndorfer et al. 10.5194/nhess-20-3135-2020
- Quantifying climate risks to infrastructure systems: A comparative review of developments across infrastructure sectors J. Verschuur et al. 10.1371/journal.pclm.0000331
- Development of multiclass alternating decision trees based models for landslide susceptibility mapping B. Pham et al. 10.1016/j.pce.2022.103235
- Rainfall-induced transportation embankment failure: A review X. Linrong et al. 10.1515/geo-2022-0558
- A machine learning approach in spatial predicting of landslides and flash flood susceptible zones for a road network H. Ha et al. 10.1007/s40808-022-01384-9
Latest update: 14 Dec 2024
Short summary
Landslides are destructive events, threatening the integrity of land transport systems. This paper presents how road networks are vulnerable to landslides, with emphasis on the consequences for affected road users. Results show the merits of using agent-based traffic modelling to assess the impacts of road network interruptions on rural communities by providing insights into the characteristics of the population affected and the effects on its daily routine in terms of detour costs.
Landslides are destructive events, threatening the integrity of land transport systems. This...
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