Skip to main content

Advertisement

Log in

Rock fall susceptibility assessment along a mountainous road: an evaluation of bivariate statistic, analytical hierarchy process and frequency ratio

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Few studies have been conducted for susceptibility of rock falls in mountainous areas. In this study, we compare and evaluate rock fall susceptibility mapping using bivariate statistical [weight of evidence (WoE)], analytical hierarchy process (AHP) and frequency ratio (FR) methods along 11 km of a mountainous road in the Salavat Abad saddle in southwestern Kurdistan, Iran. A total of 34 rock fall locations were constructed from various sources. These rock fall locations were then partitioned into a training dataset (70% of the rock fall locations) and a testing dataset (30% of the rock fall locations). Eight conditioning factors affecting on the rock falls including slope angle, aspect, curvature, elevation, distance to road, distance to fault, lithology and land use were identified. The modeling process and rock fall susceptibility mapping has been constructed using three methods. The performance of rock fall susceptibility mapping was evaluated using the area under the curve of success rate curve for training and prediction rate curves (PRC) for testing datasets and also seed cell area index. The results show that the rock fall susceptibility mapping using the WOE method has better prediction accuracy than the AHP and FR methods. Ultimately, the weight-of-evidence method is a promising technique so that it is proposed to manage and mitigate the damages of rock falls in the prone areas.

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

Similar content being viewed by others

References

  • Agterberg FP (1992) Combining indicator patterns in weights of evidence modeling for resource evaluation. Nonrenew Resour 1:39–50

    Article  Google Scholar 

  • Althuwaynee OF, Pradhan B, Park H-J, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114:21–36

    Article  Google Scholar 

  • Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: part II GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Eng Geol 81:432–445

    Article  Google Scholar 

  • Barbieri G, Cambuli P (2009) The weight of evidence statistical method in landslide susceptibility mapping of the Rio Pardu Valley (Sardinia, Italy). In: Proceedings of, 2009, pp 2658–2664

  • Bonham-Carter GF (1994) Geographic information systems for geoscientists-modeling with GIS. Comput Methods Geosci 13:398

    Google Scholar 

  • Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf. Process Landf 16:427–445

    Article  Google Scholar 

  • Chau K, Sze Y, Fung M, Wong W, Fong E, Chan L (2004) Landslide hazard analysis for Hong Kong using landslide inventory and GIS. Comput Geosci 30:429–443

    Article  Google Scholar 

  • Choi J, Oh H-J, Lee H-J, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23

    Article  Google Scholar 

  • Corominas J, Santacana N (2003) Stability analysis of the Vallcebre translational slide, Eastern Pyrenees (Spain) by means of a GIS. Nat Hazards 30:473–485

    Article  Google Scholar 

  • Corsini A, Cervi F, Ronchetti F (2009) Weight of evidence and artificial neural networks for potential groundwater spring mapping: an application to the Mt. Modino area (Northern Apennines, Italy). Geomorphology 111:79–87

    Article  Google Scholar 

  • Dai F, Lee C, Ngai YY (2002) Landslide risk assessment and management: an overview. Eng Geol 64:65–87

    Article  Google Scholar 

  • D’Amato J, Hantz D, Guerin A, Jaboyedoff M, Baillet L, Mariscal A (2016) Influence of meteorological factors on rockfall occurrence in a middle mountain limestone cliff. Nat Hazards Earth Syst Sci 16:719–735

    Article  Google Scholar 

  • Delonca A, Gunzburger Y, Verdel T (2014) Statistical correlation between meteorological and rockfall databases. Nat Hazards Earth Syst Sci 14:1953–1964

    Article  Google Scholar 

  • Dussauge-Peisser C, Helmstetter A, Grasso J-R, Hantz D, Desvarreux P, Jeannin M, Giraud A (2002) Probabilistic approach to rock fall hazard assessment: potential of historical data analysis. Nat Hazards Earth Syst Sci 2:15–26

    Article  Google Scholar 

  • Estoque RC (2012) Analytic hierarchy process in geospatial analysis. In: Murayama Y (ed) Progress in geospatial analysis. Springer, Japan, pp 157–181

    Chapter  Google Scholar 

  • Greco R, Sorriso-Valvo M, Catalano E (2007) Logistic regression analysis in the evaluation of mass movements susceptibility: the Aspromonte case study, Calabria, Italy. Eng Geol 89:47–66

    Article  Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:181–216

    Article  Google Scholar 

  • Hallet B (2006) Why do freezing rocks break? Science 314:1092–1093

    Article  Google Scholar 

  • Hantz D, Vengeon J, Dussauge-Peisser C (2003) An historical, geomechanical and probabilistic approach to rock-fall hazard assessment. Nat Hazards Earth Syst Sci 3:693–701

    Article  Google Scholar 

  • Hasekioğulları GD, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Nat Hazards 63:1157–1179

    Article  Google Scholar 

  • Hegg C, Kienholz H (1995) Determining paths of gravity-driven slope processes: the ‘Vector Tree Model’. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Springer, Netherlands, pp 79–92

    Chapter  Google Scholar 

  • Hungr O, Evans S (1988) Engineering evaluation of fragmental rockfall hazards. In: 5th International symposium on landslides, pp 685–690

  • Hussin HY, Zumpano V, Reichenbach P, Sterlacchini S, Micu M, van Westen C, Bălteanu D (2016) Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model. Geomorphology 253:508–523

    Article  Google Scholar 

  • Iovine GG, Greco R, Gariano SL, Pellegrino AD, Terranova OG (2014) Shallow-landslide susceptibility in the Costa Viola mountain ridge (southern Calabria, Italy) with considerations on the role of causal factors. Nat Hazards 73:111–136

    Article  Google Scholar 

  • Kayastha P, Dhital M, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408

    Article  Google Scholar 

  • Kelarestaghi A, Ahmadi H (2009) Landslide susceptibility analysis with a bivariate approach and GIS in Northern Iran. Arab J Geosci 2:95–101

    Article  Google Scholar 

  • Keylock C, Domaas U (1999) Evaluation of topographic models of rockfall travel distance for use in hazard applications. Arct Antarct Alp Res 31:312–320

    Article  Google Scholar 

  • Kıncal C, Akgun A, Koca MY (2009) Landslide susceptibility assessment in the Izmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method. Environ Earth Sci 59:745–756

    Article  Google Scholar 

  • Kirkby M, Statham I (1975) Surface stone movement and scree formation. J Geol 83:349–362

    Article  Google Scholar 

  • Kobayashi Y, Harp E, Kagawa T (1990) Simulation of rockfalls triggered by earthquakes. Rock Mech Rock Eng 23:1–20

    Article  Google Scholar 

  • Krautblatter M, Moser M (2009) A nonlinear model coupling rockfall and rainfall intensity based newline on a four year measurement in a high Alpine rock wall (Reintal, German Alps). Nat Hazards Earth Syst Sci 9:1425–1432

    Article  Google Scholar 

  • Lee S (2004) Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environ Manag 34:223–232

    Article  Google Scholar 

  • Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26:1477–1491

    Article  Google Scholar 

  • Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area Cambodia using frequency ratio and logistic regression models. Environ Geol 50:847–855

    Article  Google Scholar 

  • Lu P, Rosenbaum M (2003) Artificial neural networks and grey systems for the prediction of slope stability. Nat Hazards 30:383–398

    Article  Google Scholar 

  • Luckman B, Fiske C (1995) Estimating long-term rockfall accretion rates by lichenometry Steepland. Geomorphology 3:221–254

    Google Scholar 

  • Matsuoka N, Sakai H (1999) Rockfall activity from an alpine cliff during thawing periods. Geomorphology 28:309–328

    Article  Google Scholar 

  • Oh H-J, Lee S, Chotikasathien W, Kim CH, Kwon JH (2009) Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand. Environ Geol 57:641–651

    Article  Google Scholar 

  • Ozdemir A (2011) Landslide susceptibility mapping using Bayesian approach in the Sultan Mountains (Akşehir, Turkey). Nat Hazards 59:1573–1607

    Article  Google Scholar 

  • Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area Korea. Environ Earth Sci 68:1443–1464

    Article  Google Scholar 

  • Petschko H, Brenning A, Bell R, Goetz J, Glade T (2014) Assessing the quality of landslide susceptibility maps–case study lower Austria. Nat Hazards Earth Syst Sci 14:95–118

    Article  Google Scholar 

  • Pfeiffer TJ, Bowen TD (1989) Computer simulation of rockfalls. Bull Assoc Eng Geol 26:135–146

    Google Scholar 

  • Pourtaghi ZS, Pourghasemi HR (2014) GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province Iran. Hydrogeol J 22:643–662

    Article  Google Scholar 

  • Pradhan B (2010) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38:301–320

    Article  Google Scholar 

  • Quan H-C, Lee B-G (2012) GIS-based landslide susceptibility mapping using analytic hierarchy process and artificial neural network in Jeju (Korea). KSCE J Civ Eng 16:1258–1266

    Article  Google Scholar 

  • Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7:725–742

    Article  Google Scholar 

  • Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:234–281

    Article  Google Scholar 

  • Saaty T (1980) The analytic hierarchy process—what it is and how it is used. Math Model 9:161–176

    Article  Google Scholar 

  • Sandersen F, Bakkehøi S, Hestnes E, Lied K (1997) The influence of meteorological factors on the initiation of debris flows, rockfalls, rockslides and rockmass stability Publikasjon-Norges Geotekniske Institutt 201 pp 97–114

  • Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Sci Rep 5. doi:10.1038/srep09899

  • Shahabi H, Ahmad B, Khezri S (2013) Evaluation and comparison of bivariate and multivariate statistical methods for landslide susceptibility mapping (case study: Zab basin). Arab J Geosci 6:3885–3907

    Article  Google Scholar 

  • Shahabi H, Khezri S, Ahmad BB, Hashim M (2014) Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena 115:55–70

    Article  Google Scholar 

  • Shirzadi A, Saro L, Joo OH, Chapi K (2012) A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study Kurdistan, Iran. Nat Hazards 64:1639–1656

    Article  Google Scholar 

  • Sturzenegger M, Sartori M, Jaboyedoff M, Stead D (2007) Regional deterministic characterization of fracture networks and its application to GIS-based rock fall risk assessment. Eng Geol 94:201–214

    Article  Google Scholar 

  • Süzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45:665–679

    Article  Google Scholar 

  • Uribe-Etxebarria G, Morales T, Uriarte JA, Ibarra V (2005) Rock cut stability assessment in mountainous regions. Environ Geol 48:1002–1013

    Article  Google Scholar 

  • Vargas LG (1990) An overview of the analytic hierarchy process and its applications. Eur J Oper Res 48:2–8

    Article  Google Scholar 

  • Whalley W (1984) Rockfalls Slope instability:217-256

  • Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena 72:1–12

    Article  Google Scholar 

  • Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79:251–266

    Article  Google Scholar 

  • Zimmer VL, Collins BD, Stock GM, Sitar N (2012) Rock fall dynamics and deposition: an integrated analysis of the 2009 Ahwiyah Point rock fall Yosemite National Park, USA. Earth Surf Process Landf 37:680–691

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to express their sincere thanks to the Forests, Range & Watershed Management Organization of Iran (FRWMO) for provide various datasets and Universiti Teknologi Malaysia (UTM) based on Research University Grant (Q.J130000.2527.12H65) and University of Kurdistan, Iran for their financial supports in this research. Also, the authors would like to acknowledge the anonymous reviewers and editor for their helpful comments on a previous version of the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Himan Shahabi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shirzadi, A., Chapi, K., Shahabi, H. et al. Rock fall susceptibility assessment along a mountainous road: an evaluation of bivariate statistic, analytical hierarchy process and frequency ratio. Environ Earth Sci 76, 152 (2017). https://doi.org/10.1007/s12665-017-6471-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-017-6471-6

Keywords

Navigation