Study on Road Network Vulnerability Considering the Risk of Landslide Geological Disasters in China's Tibet

Road traffic is occasionally blocked by landslide geological disasters in remote mountainous areas, causing obstruction to economic society and national defense construction. It is vital to conduct landslide geological disaster risk assessment and vulnerability research on the road network. Based on landslide geological disaster risk on the road network, this study analyzed the potential effects of the main environmental elements. Due to the lack of previous research works, this study proposed an effective, rational, and understandable multicriteria heuristic analytical hierarchy process model, fuzzy comprehensive evaluation, and frequency ratio-interactive fuzzy stack analysis for vulnerability assessment of road networks in large and complex networks. Based on the comprehensive use of geographic information technology, the road network vulnerability of Tibet in China was evaluated by introducing slope, topographic relief, normalized difference vegetation index (NDVI), annual mean precipitation, distance from river drainage, glaciers and snow, habitation, seismic center and geological fault zone, and soil erosion intensity. According to the findings of the study, the three-stage framework proposed in this study can provide correct inferences and explanations for the potential phenomena of landslide geological disasters; the geological disaster risk are unevenly distributed in the study area; the distribution of the road network vulnerability in China’s Tibet significantly differs among different cities; the high-vulnerability section presents significant regional characteristics, which overlap with the area with a high risk of landslide geological disasters, and its distribution is mostly located in traffic arteries, link aggregations, and relatively frequent human activity.

[1]  F. Mohamed Nazri,et al.  Integrated seismic vulnerability assessment of road network in complex built environment toward more resilient cities , 2023, Sustainable Cities and Society.

[2]  N. Alias,et al.  Discovering the global landscape of vulnerability assessment method of transportation network studies: A bibliometric review , 2023, Physics and Chemistry of the Earth, Parts A/B/C.

[3]  W. Jian,et al.  Spatial prediction of the geological hazard vulnerability of mountain road network using machine learning algorithms , 2023, Geomatics, Natural Hazards and Risk.

[4]  Liu Hong,et al.  An approach for accessibility assessment and vulnerability analysis of national multimodal transport systems , 2023, Risk analysis : an official publication of the Society for Risk Analysis.

[5]  L. Bryson,et al.  Landslide Risk Assessment in Eastern Kentucky, USA: Developing a Regional Scale, Limited Resource Approach , 2022, Remote. Sens..

[6]  R. Hewson,et al.  Evaluation of the modified AHP-VIKOR for mapping and ranking copper mineralized areas, a case study from the Kerman metallogenic belt, SE Iran , 2022, Arabian Journal of Geosciences.

[7]  Hernán E. De Solminihac,et al.  Risk Management System for Road Networks Exposed to Natural Hazards , 2022, Lifelines 2022.

[8]  Zhigang Han,et al.  Vulnerability Identification and Cascading Failure Spatiotemporal Patterns on Road Network under the Rainstorm Disaster , 2022, ISPRS Int. J. Geo Inf..

[9]  Nur Sabahiah Abdul Sukor,et al.  Vulnerability of road transportation networks under natural hazards: A bibliometric analysis and review , 2022, International Journal of Disaster Risk Reduction.

[10]  L. Leong,et al.  An Integrated Framework for the Quantification of Road Network Seismic Vulnerability and Accessibility to Critical Services , 2022, Sustainability.

[11]  A. Tang,et al.  Total probabilistic measure for the potential risk of regional roads exposed to landslides , 2022, Reliab. Eng. Syst. Saf..

[12]  Qing Zhu,et al.  Graph neural networks with constraints of environmental consistency for landslide susceptibility evaluation , 2022, Int. J. Geogr. Inf. Sci..

[13]  Xinyi Feng,et al.  A Landslide Susceptibility Evaluation of Highway Disasters Based on the Frequency Ratio Coupling Model , 2022, Sustainability.

[14]  Juanjuan Lin,et al.  Transportation System Vulnerability Assessment considering Environmental Impact , 2022, Journal of Advanced Transportation.

[15]  Nan Wang,et al.  Assessing the risk posed by flash floods to the transportation network in southwestern China , 2022, Geocarto International.

[16]  D. T. Tran,et al.  A machine learning approach in spatial predicting of landslides and flash flood susceptible zones for a road network , 2022, Modeling Earth Systems and Environment.

[17]  Abbas Abbaszadeh Shahri,et al.  A Novel Approach to Uncertainty Quantification in Groundwater Table Modeling by Automated Predictive Deep Learning , 2022, Natural Resources Research.

[18]  Tao Chen,et al.  A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[19]  M. Ercanoglu,et al.  A novel data-driven approach to pairwise comparisons in AHP using fuzzy relations and matrices for landslide susceptibility assessments , 2022, Environmental Earth Sciences.

[20]  Yufeng He,et al.  Modeling vague spatiotemporal objects based on interval type-2 fuzzy sets , 2022, Int. J. Geogr. Inf. Sci..

[21]  Rana Waqar Aslam,et al.  Landslide hazard, susceptibility and risk assessment (HSRA) based on remote sensing and GIS data models: a case study of Muzaffarabad Pakistan , 2022, Stochastic Environmental Research and Risk Assessment.

[22]  Norbazlan Mohd Yusof,et al.  Road Network Vulnerability Based on Diversion Routes to Reconnect Disrupted Road Segments , 2022, Sustainability.

[23]  P. Reichenbach,et al.  National and regional-scale landslide indicators and indexes: Applications in Italy , 2022, Open Geosciences.

[24]  Desh Deepak Pandey,et al.  Landslide susceptibility assessment in the himalayan range based along kasauli – parwanoo road corridor using weight of evidence, information value, and frequency ratio , 2021, Journal of King Saud University - Science.

[25]  Ghassan Beydoun,et al.  Spatial earthquake vulnerability assessment by using multi-criteria decision making and probabilistic neural network techniques in Odisha, India , 2021, Geocarto International.

[26]  Luis F. Robledo,et al.  Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China , 2021, Natural Hazards.

[27]  D. Giordan,et al.  Rockfall susceptibility along the regional road network of Aosta Valley Region (northwestern Italy) , 2020 .

[28]  Yi Wang,et al.  A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping , 2020, Int. J. Geogr. Inf. Sci..

[29]  L. Ortolano,et al.  When floods hit the road: Resilience to flood-related traffic disruption in the San Francisco Bay Area and beyond , 2020, Science Advances.

[30]  Guohui Zhang,et al.  Assessing potential likelihood and impacts of landslides on transportation network vulnerability , 2020 .

[31]  Xin Li,et al.  Research on Identification Method of Key Road Sections in the Road Network under Disaster Situation , 2020, 2020 5th International Conference on Electromechanical Control Technology and Transportation (ICECTT).

[32]  E. Yan,et al.  Evaluating landslide susceptibility based on cluster analysis, probabilistic methods, and artificial neural networks , 2020, Bulletin of Engineering Geology and the Environment.

[33]  Johan Spross,et al.  Landslide susceptibility hazard map in southwest Sweden using artificial neural network , 2019 .

[34]  Yi Qiang,et al.  Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data , 2019, Int. J. Geogr. Inf. Sci..

[35]  P. Bernatchez,et al.  Quantifying road vulnerability to coastal hazards: Development of a synthetic index , 2019, Ocean & Coastal Management.

[36]  Michael G H Bell,et al.  Attacker-defender model against quantal response adversaries for cyber security in logistics management: An introductory study , 2019, Eur. J. Oper. Res..

[37]  Melih Çelik,et al.  Pre-positioning of relief items under road/facility vulnerability with concurrent restoration and relief transportation , 2019, IISE Trans..

[38]  Hong Zhang,et al.  An Integrative Vulnerability Evaluation Model to Urban Road Complex Network , 2019, Wirel. Pers. Commun..

[39]  B. Pradhan,et al.  Modification of landslide susceptibility mapping using optimized PSO-ANN technique , 2018, Engineering with Computers.

[40]  Saro Lee,et al.  Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea , 2018 .

[41]  Javed Mallick,et al.  GIS-based landslide susceptibility evaluation using fuzzy-AHP multi-criteria decision-making techniques in the Abha Watershed, Saudi Arabia , 2018, Environmental Earth Sciences.

[42]  Hamid Reza Pourghasemi,et al.  Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China , 2018, Comput. Geosci..

[43]  Ping Zhang,et al.  Application of fuzzy comprehensive evaluation to evaluate the effect of water flooding development , 2018, Journal of Petroleum Exploration and Production Technology.

[44]  Nicola Casagli,et al.  A GIS-Based Procedure for Landslide Intensity Evaluation and Specific risk Analysis Supported by Persistent Scatterers Interferometry (PSI) , 2017, Remote. Sens..

[45]  Roberta Pellicani,et al.  GIS-based predictive models for regional-scale landslide susceptibility assessment and risk mapping along road corridors , 2017 .

[46]  Mei Liu,et al.  Vulnerability of road networks , 2016 .

[47]  Xu Wu,et al.  Analysis and research on the influencing factor of the road transportation network vulnerability based on the interpretative structural model , 2015, 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS).

[48]  Federico Rupi,et al.  The Evaluation of Road Network Vulnerability in Mountainous Areas: A Case Study , 2015 .

[49]  Albert P.C. Chan,et al.  Recent advances in modeling the vulnerability of transportation networks , 2015 .

[50]  Mustafa Neamah Jebur,et al.  Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia , 2014 .

[51]  Diofantos G. Hadjimitsis,et al.  Integrated use of GIS and remote sensing for monitoring landslides in transportation pavements: the case study of Paphos area in Cyprus , 2014, Natural Hazards.

[52]  Manfred F. Buchroithner,et al.  A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses , 2010, Comput. Environ. Urban Syst..

[53]  Min Ouyang,et al.  A methodological approach to analyze vulnerability of interdependent infrastructures , 2009, Simul. Model. Pract. Theory.

[54]  Alan T. Murray,et al.  A Methodological Overview of Network Vulnerability Analysis , 2008 .

[55]  Anthony Chen,et al.  Network-based Accessibility Measures for Vulnerability Analysis of Degradable Transportation Networks , 2007 .

[56]  Glen M. D'Este,et al.  Application of Accessibility Based Methods for Vulnerability Analysis of Strategic Road Networks , 2006 .

[57]  Jungyul Sohn,et al.  Evaluating the significance of highway network links under the flood damage: An accessibility approach , 2006 .

[58]  L. Ayalew,et al.  Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications , 2005 .

[59]  Katja Berdica,et al.  AN INTRODUCTION TO ROAD VULNERABILITY: WHAT HAS BEEN DONE, IS DONE AND SHOULD BE DONE , 2002 .

[60]  P. Reichenbach,et al.  Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy , 1999 .

[61]  Alan Nicholson,et al.  DEGRADABLE TRANSPORTATION SYSTEMS: SENSITIVITY AND RELIABILITY ANALYSIS , 1997 .

[62]  S. Saravanan,et al.  Landslide susceptibility assessment using frequency ratio technique – A case study of NH67 road corridor in the Nilgiris district, Tamilnadu, India , 2021, IOP Conference Series: Earth and Environmental Science.

[63]  Rocco Palamara,et al.  A fuzzy methodology to evaluate the landslide risk in road lifelines , 2020, Transportation Research Procedia.

[64]  Li Xu,et al.  The second Chinese glacier inventory: data, methods and results , 2015 .

[65]  Min Ouyang,et al.  Vulnerability analysis of interdependent infrastructure systems under edge attack strategies , 2013 .

[66]  Kay W. Axhausen,et al.  Vulnerability Assessment Methodology for Swiss Road Network , 2009 .

[67]  Szymon Wiśniewski,et al.  The impact of self-evacuation from flood hazard areas on the equilibrium of the road transport , 2022, Safety Science.