Skip to main content

Advertisement

Log in

Vulnerability assessment of road networks to landslide hazards in a dry-mountainous region

  • Thematic Issue
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Landslides are natural hazards that can cause catastrophic life losses and damage to infrastructures and communities. In Iran, landslide exposure has been predominantly increasing in the Zagros Mountains, notably along the lifelines, such as road networks. Therefore, this study aimed to investigate the landslide vulnerability of a 6682 km road network in the Chaharmahal and Bakhtiari Province, Iran, using a two-step methodology comprised of: (1) landslide susceptibility mapping using four machine learning methods—boosted regression trees (BRT), multiple discriminant analysis (MDA), multivariate adaptive regression splines (MARS), and random forest (RF); and (2) mapping road exposure to landslides using the analytic hierarchy process (AHP) that computed the weight for four buffer zones (0–50, 50–150, 150–300, and > 300 m) from the road network. The combined results of steps 1 and 2 produced a map of the road network vulnerability to landslides that demonstrated that 9.7 km (13.6%) of the road network was located in the very-high vulnerability class. Specifically, the roads of the Ardal and Kohrang counties have been found to be the most vulnerable to landslide risk. The finding of this study could be useful for decision-makers and civil engineering to better manage road networks in terms of landslide risk and community resilience in the aftermath of major landslides.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Abdulelah Al-Sudani Z, Salih SQ, Sharafati A, Yaseen ZM (2019) Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation. J Hydrol 573:1–12

    Article  Google Scholar 

  • Alexander JS, Peter L, Barlett B, Scholkopf DS (2000) Advanced in Large Margin Classifiers. MIT press, Cambridge

    Google Scholar 

  • Avand M, Janizadeh S, Naghibi SA et al (2019) A comparative assessment of random forest and k-nearest neighbor classifiers for gully erosion susceptibility mapping. Water (Switzerland) 11:2076

    Google Scholar 

  • Bashir S, Carter EM (2005) High breakdown mixture discriminant analysis. J Multivar Anal 93:102–111

    Article  Google Scholar 

  • Blaschke PM, Trustrum NA, Hicks DL (2000) Impacts of mass movement erosion on land productivity: a review. Prog Phys Geogr 24:21–52

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Byles R (1993) Mass movement. New Civ Eng 1046:18–19

    Google Scholar 

  • Chaytor JD, Twichell DC, Ten Brink US et al. (2007) Revisiting submarine mass movements along the US Atlantic continental margin: implications for tsunami hazards. In: Submarine mass movements and their consequences, 3rd International Symposium. Springer, pp 395–403

  • Chen J, Du L, Guo Y (2021) Label constrained convolutional factor analysis for classification with limited training samples. Inform Sci 544:372–394

    Article  Google Scholar 

  • Chen Z, Liu Z, Yin L, Zheng W (2022) Statistical analysis of regional air temperature characteristics before and after dam construction. Urban Clim 41:101085

    Article  Google Scholar 

  • Choubin B, Borji M, Mosavi A et al (2019) Snow avalanche hazard prediction using machine learning methods. J Hydrol 577:123929

    Article  Google Scholar 

  • Cignetti M, Godone D, Bertolo D et al (2021) Rockfall susceptibility along the regional road network of Aosta Valley Region (northwestern Italy). J Maps 17:54–64

    Article  Google Scholar 

  • de Jesus JB, Kuplich TM, de Carvalho BÍD, da Rosa CN, Hillebrand FL (2021) Temporal and phenological profiles of open and dense Caatinga using remote sensing: response to precipitation and its irregularities. J Forest Res 32(3):1067–1076

    Article  Google Scholar 

  • Deichmann J, Eshghi A, Haughton D et al (2002) Application of multiple adaptive regression splines (mars) in direct response modeling. J Interact Mark 16:15–27

    Article  Google Scholar 

  • Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77:802–813

    Article  Google Scholar 

  • Esposito G, Carabella C, Paglia G, Miccadei E (2021) Relationships between morphostructural/geological framework and landslide types: Historical landslides in the hilly piedmont area of abruzzo region (central Italy). Land 10:287

    Article  Google Scholar 

  • Ferlisi S, Marchese A, Peduto D (2021) Quantitative analysis of the risk to road networks exposed to slow-moving landslides: a case study in the Campania region (southern Italy). Landslides 18(1):303–319

    Article  Google Scholar 

  • Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49

    Article  Google Scholar 

  • Foong LK, Zhao Y, Bai C, Xu C (2021) Efficient metaheuristic-retrofitted techniques for concrete slump simulation. Smart Struct Sys Int J 27(5):745–759

    Google Scholar 

  • Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28:337–407

    Article  Google Scholar 

  • Gu C, Wahba G (1991) Discussion: multivariate adaptive regression splines. Ann Stat 19:115–123

    Article  Google Scholar 

  • Gutierrez RR, Abad JD, Choi M, Montoro H (2014) Characterization of confluences in free meandering rivers of the Amazon basin. Geomorphology 220:1–14

    Article  Google Scholar 

  • Halbe Z, Aladjem M (2007) Regularized mixture discriminant analysis. Pattern Recognit Lett 28:2104–2115

    Article  Google Scholar 

  • Huang S, Huang M, Lyu Y (2021) Seismic performance analysis of a wind turbine with a monopile foundation affected by sea ice based on a simple numerical method. Eng Appl Computational Fluid Mech 15(1):1113–1133

    Article  Google Scholar 

  • Jaafari A, Panahi M, Mafi-Gholami D, Rahmati O, Shahabi H, Shirzadi A, Lee S, Bui DT, Pradhan B (2022) Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides. Appl Soft Comput 116:108254

    Article  Google Scholar 

  • Kjekstad O, Highland L (2009) Economic and social impacts of landslides. Landslides–disaster risk reduction. Springer, Berlin, pp 573–587

    Chapter  Google Scholar 

  • Korup O, Stolle A (2014) Landslide prediction from machine learning. Geol Today 30:26–33

    Article  Google Scholar 

  • Leonardi G, Palamara R, Suraci F (2020) A fuzzy methodology to evaluate the landslide risk in road lifelines. Transp Res Procedia 45:732–739

    Article  Google Scholar 

  • Li J, Cheng F, Lin G, Wu C (2022a) Improved hybrid method for the generation of ground motions compatible with the multi-damping design spectra. J Earthquake Eng 9:1–27

    Google Scholar 

  • Li Q, Song D, Yuan C, Nie W (2022b) An image recognition method for the deformation area of open-pit rock slopes under variable rainfall. Measurement 188:110544

    Article  Google Scholar 

  • Lin Z, Wang H, Li S (2022) Pavement anomaly detection based on transformer and self-supervised learning. Autom Construct 143:104544

    Article  Google Scholar 

  • Liu Y, Tian J, Zheng W, Yin L (2022) Spatial and temporal distribution characteristics of haze and pollution particles in China based on spatial statistics. Urban Clim 41:101031

    Article  Google Scholar 

  • Luo Z, Wang H, Li S (2022) Prediction of international roughness index based on stacking fusion model. Sustainability 14(12):6949

    Article  Google Scholar 

  • Mafi-Gholami D, Zenner EK, Jaafari A, Bakhtyari HRR, Bui DT (2019) Multi-hazards vulnerability assessment of southern coasts of Iran. J Environ Manag 252:109628

    Article  Google Scholar 

  • Merow C, Smith MJ, Silander JA (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography (cop) 36:1058–1069

    Article  Google Scholar 

  • Moayedi H, Mehrabi M, Mosallanezhad M et al (2019) Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput 35:967–984

    Article  Google Scholar 

  • Moeyersons J, Van Den Eeckhaut M, Nyssen J et al (2008) Mass movement mapping for geomorphological understanding and sustainable development: Tigray, Ethiopia. CATENA 75:45–54

    Article  Google Scholar 

  • Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models. J Asian Earth Sci 61:221–236

    Article  Google Scholar 

  • Morris K, McNicholas PD (2016) Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures. Comput Stat Data Anal 97:133–150

    Article  Google Scholar 

  • Orrego S, Montes C, Restrepo HI, Bullock BP, Zapata M (2021) Modeling height growth for teak plantations in Colombia using the reducible stochastic differential equation approach. J Forest Res 32(3):1035–1045

    Article  Google Scholar 

  • Özel C, Güner ŞT, Türkkan M, Akgül S, Şentürk Ö (2021) Modelling the site index of Pinus pinaster plantations in Turkey using ecological variables. J Forest Res 32(2):589–598

    Article  Google Scholar 

  • Pohl W (1997) LaboUr—machine learning for user modeling. Adv Hum Factors/Ergonon 21:27–30

    Google Scholar 

  • Pourghasemi HR, Mohammady M, Pradhan B (2012) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84

    Article  Google Scholar 

  • Quan Q, Gao S, Shang Y, Wang B (2021) Assessment of the sustainability of Gymnocypris eckloni habitat under river damming in the source region of the Yellow River. Sci Tot Environ 778:146312

    Article  Google Scholar 

  • Ruppert D (2004) The elements of statistical learning: data mining, inference, and prediction. J Am Stat Assoc 99(466):567

    Article  Google Scholar 

  • Santos M, Aguiar M, Oliveira A et al (2020) Vulnerability to mass movements’ hazards contribution of sociology to increasing community resilience. Advances in natural hazards and hydrological risks: meeting the challenge. Springer, Berlin, pp 105–108

    Chapter  Google Scholar 

  • Shabani S, Jaafari A, Bettinger P (2021) Spatial modeling of forest stand susceptibility to logging operations. Environ Impact Assess Rev 89:106601

    Article  Google Scholar 

  • Shen X, Hong Y, Zhang K, Hao Z (2017) Refining a distributed linear reservoir routing method to improve performance of the CREST model. J Hydrology Eng 22(3):04016061

    Article  Google Scholar 

  • Stoffel M, Huggel C (2012) Effects of climate change on mass movements in mountain environments. Prog Phys Geogr 36:421–439

    Article  Google Scholar 

  • Taalab K, Cheng T, Zhang Y (2018) Mapping landslide susceptibility and types using random forest. Big Earth Data 2:159–178

    Article  Google Scholar 

  • Tian H, Huang N, Niu Z, Qin Y, Pei J, Wang J (2019) Mapping winter crops in China with multi-source satellite imagery and phenology-based algorithm. Rem Sens 11(7):820

    Article  Google Scholar 

  • Tian H, Wang Y, Chen T, Zhang L, Qin Y (2021) Early-season mapping of winter crops using sentinel-2 optical imagery. Rem Sens 13(19):3822

    Article  Google Scholar 

  • Tian H, Pei J, Huang J, Li X, Wang J, Zhou B, Wang L (2020) Garlic and winter wheat identification based on active and passive satellite imagery and the google earth engine in northern china. Rem Sens 12(21):3539

    Article  Google Scholar 

  • Valjarević A, Djekić T, Stevanović V et al (2018) GIS numerical and remote sensing analyses of forest changes in the Toplica region for the period of 1953–2013. Appl Geogr 92:131–139

    Article  Google Scholar 

  • Valjarević A, Filipović D, Valjarević D et al (2020) GIS and remote sensing techniques for the estimation of dew volume in the Republic of Serbia. Meteorol Appl 27:e1930

    Article  Google Scholar 

  • Wang S, Zhang K, Chao L, Li D, Tian X, Bao H, Chen G, Xia Y (2021) Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards. J Hydrol 603:126964

    Article  Google Scholar 

  • Winter MG, Shearer B, Palmer D, Peeling D, Harmer C, Sharpe J (2016) The economic impact of landslides and floods on the road network. Procedia Eng 143:1425–1434

    Article  Google Scholar 

  • Wohlers A, Damm B (2022) Rockfall vulnerability of a rural road network—a methodological approach in the harz mountains, Germany. Geosciences 12(4):170

    Article  Google Scholar 

  • Xie W, Li X, Jian W, Yang Y, Liu H, Robledo LF, Nie W (2021a) A novel hybrid method for landslide susceptibility mapping-based geodetector and machine learning cluster: a case of Xiaojin county, China. ISPRS Int J Geo-Inform 10(2):93

    Article  Google Scholar 

  • Xie W, Nie W, Saffari P, Robledo LF, Descote PY, Jian W (2021b) Landslide hazard assessment based on bayesian optimization–support vector machine in Nanping City, China. Nat Haz 109(1):931–948

    Article  Google Scholar 

  • Yan B, Ma C, Zhao Y, Hu N, Guo L (2019) Geometrically enabled soft electroactuators via laser cutting. Adv Eng Mater 21(11):1900664

    Article  Google Scholar 

  • Yin L, Wang L, Keim BD, Konsoer K, Zheng W (2022) Wavelet analysis of dam injection and discharge in three gorges dam and reservoir with precipitation and river discharge. Water 14(4):567

    Article  Google Scholar 

  • Yousefi S, Mirzaee S, Almohamad H et al (2022) Image classification and land cover mapping using sentinel-2 imagery: optimization of SVM parameters. Land 11:993

    Article  Google Scholar 

  • Zhang K, Wang S, Bao H, Zhao X (2019) Characteristics and influencing factors of rainfall–induced landslide and debris flow hazards in Shaanxi Province, China. Nat Haz Earth Sys Sci 19(1):93–105

    Article  Google Scholar 

  • Zhang Q, Yu H, Li Z, Zhang G, Ma DT (2020a) Assessing potential likelihood and impacts of landslides on transportation network vulnerability. Transp Res Transp Environ 82:102304

    Article  Google Scholar 

  • Zhang Z, Luo C, Zhao Z (2020b) Application of probabilistic method in maximum tsunami height prediction considering stochastic seabed topography. Nat Haz 104(3):2511–2530

    Article  Google Scholar 

  • Zhao Y, Foong LK (2022) Predicting electrical power output of combined cycle power plants using a novel artificial neural network optimized by electrostatic discharge algorithm. Measurement 198:111405

    Article  Google Scholar 

  • Zhao Y, Hu H, Song C, Wang Z (2022) Predicting compressive strength of manufactured-sand concrete using conventional and metaheuristic-tuned artificial neural network. Measurement 194:110993

    Article  Google Scholar 

  • Zhao Y, Moayedi H, Bahiraei M, Foong LK (2020a) Employing TLBO and SCE for optimal prediction of the compressive strength of concrete. Smart Struct Sys 26(6):753–763

    Google Scholar 

  • Zhao Y, Yan Q, Yang Z, Yu X, Jia B (2020b) A novel artificial bee colony algorithm for structural damage detection. Adv Civ Eng 2020:3743089

    Google Scholar 

  • Zhao Y, Wang Z (2022) Subset simulation with adaptable intermediate failure probability for robust reliability analysis: an unsupervised learning-based approach. Struct Multidiscip Optim 65(6):1–22

    Article  Google Scholar 

  • Zhao Y, Hu H, Bai L, Tang M, Chen H, Su D (2021a) Fragility analyses of bridge structures using the logarithmic piecewise function-based probabilistic seismic demand model. Sustainability 13(14):7814

    Article  Google Scholar 

  • Zhao Y, Zhong X, Foong LK (2021b) Predicting the splitting tensile strength of concrete using an equilibrium optimization model. Steel Compos Struct 39(1):81–93

    Google Scholar 

  • Zhou G, Long S, Xu J, Zhou X, Song B, Deng R, Wang C (2021a) Comparison analysis of five waveform decomposition algorithms for the airborne LiDAR echo signal. IEEE J Sel Topics Appl Earth Observations Remote Sen 14:7869–7880

    Article  Google Scholar 

  • Zhou G, Zhang R, Huang S (2021b) Generalized buffering algorithm. IEEE Access 9:27140–27157

    Article  Google Scholar 

  • Zhu Z, Wu Y, Han J (2022) A prediction method of coal burst based on analytic hierarchy process and fuzzy comprehensive evaluation. Front Earth Sci. https://doi.org/10.3389/feart.2021.834958

    Article  Google Scholar 

Download references

Funding

This study was funded by Research Institute of Forests and Rangelands (RIFR) as part of the National Research Project No. 0-09-09-002-000095.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abolfazl Jaafari.

Ethics declarations

Conflict of interest

The authors have not disclosed any conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of a Topical Collection in Environmental Earth Sciences on “Landslides in a Changing Environment”, guest edited by Mihai Ciprian Mărgărint, Marta Jurchescu.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yousefi, S., Jaafari, A., Valjarević, A. et al. Vulnerability assessment of road networks to landslide hazards in a dry-mountainous region. Environ Earth Sci 81, 521 (2022). https://doi.org/10.1007/s12665-022-10650-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-022-10650-z

Keywords

Navigation