Skip to main content

Advertisement

SpringerLink
Log in

Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran

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

Abstract

In many parts of the world, landslide susceptibility remains inadequately mapped, due to the lack of both data and suitable methods for widespread implementation. Iran is one of those countries with extensive landslide problems, with nearly 4900 large landslides occurring between 1993 and 2007. At the same time landslide susceptibility has not been assessed for the country. Random forest (RF) has recently been shown to be a suitable tool for such mapping. In this study we further coupled the RF method with an evidential belief function (EBF) approach, and tested the suitability for landslide susceptibility mapping for variable terrain and data conditions in the west of Mazandaran Province, northern Iran. Locations of earlier landslides were identified by interpreting aerial photographs and through extensive field surveys. Eleven conditioning factors were used in the RF model. The spatial relationship between landslide occurrence and conditioning factors was then assessed using the data-driven EBF model, and EBF values paired to each map. Finally, the EBF maps were used for running the RF model. Finally, the efficiency of the RF-EBF model was tested using the area under the curve to measure the success and prediction rates of the incorporated data. This resulted in a success rate of 85.2 %, and a prediction rate of 81.8 %. The most important conditioning factors identified were lithology, altitude, distance from roads, and land use, respectively. Based on the overall assessment, the combined RF and EBF approach was found to be objective and an applicable estimator that improves the predictive accuracy and controls for overfitting, and thus useful for landslide susceptibility mapping at regional scales.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

  • Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38:23–34

    Article  Google Scholar 

  • Althuwaynee O F, Pradhan B (2014) An alternative technique for landslide inventory modeling based on spatial pattern characterization. In: Rahman AA et al. (eds) Geoinformation for Informed Decisions. Lecture notes in geoinformation and cartography, pp 35–48. doi:10.1007/978-3-319-03644-1_3

  • Baeza C, Corominas J (2001) Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Proc Landf 26:1251–1263

    Article  Google Scholar 

  • Ball RL (2009) Comparison of random forest, artificial neural network, and multi-linear regression: a water temperature prediction case. In: Seventh conference on artificial intelligence and its applications to the environmental sciences, pp 1–6,

  • Bednarik M, Magulova B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kralovany-Liptovsky Mikulas railway case study. Phys Chem Earth Parts A/B/C 35(3–5):162–171

    Article  Google Scholar 

  • Beven KJ (1997) TOPMODEL: a critique. Hydrol Process 11:1069–1086

    Article  Google Scholar 

  • Beven KJ, Freer J (2001) A dynamic TOPMODEL. Hydrol Process 15:1993–2011

    Article  Google Scholar 

  • Beven K, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69

    Article  Google Scholar 

  • Bortoloti FD, Castro Junior RM, Araújo LC, de Morais MGB (2015) Preliminary landslide susceptibility zonation using GIS-based fuzzy logic in Vitória, Brazil. Environ Earth Sci 74(3):2125–2141

    Article  Google Scholar 

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

  • Bui TD, Pradhan B, Lofman O, Revhaug I (2012) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree and Naive Bayes models. Math Probl Eng 2012:1–26. doi:10.1155/2012/974638

    Google Scholar 

  • Bui DT, Pradhan B, Revhaug I, Nguyen DB, Pham HV, Bui QN (2013) A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). Geomat Nat Hazards Risk. doi:10.1080/19475705.2013.843206

    Google Scholar 

  • Calle ML, Urrea V (2010) Letter to the Editor: Stability of random forest importance measures. Brief Bioinform 12(1):86–89

    Article  Google Scholar 

  • Carranza EJM (2009) Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features. Ore Geol Rev 35:383–400

    Article  Google Scholar 

  • Carranza EJM, Hale M (2002) Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geol Rev 22:117–132

    Article  Google Scholar 

  • Carranza EJM, Woldai T, Chikambwe EM (2005) Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi District, Zambia. Nat Resour Res 14:47–63

    Article  Google Scholar 

  • Carranza EJM, van Ruitenbeek FJA, Hecker C, van der Meijde M, van der Meer FD (2008) Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. Int J Appl Earth Obs Geoinf 10:374–387

    Article  Google Scholar 

  • Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazards Earth Syst Sci 13:2815–2831

    Article  Google Scholar 

  • Chen T, Niu R, Du B, Wang Y (2014) Landslide spatial susceptibility mapping by using GIS and remote sensing techniques: a case study in Zigui County, the Three Georges reservoir, China. Environ Earth Sci 73(9):5571–5583

    Article  Google Scholar 

  • Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63:397–406

    Article  Google Scholar 

  • Cutler A (2013) Trees and random forests. NIH 1R15AG037392-01, p 92

  • Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random forests for classification in Ecology. Ecology 88(11):2783–2792

    Article  Google Scholar 

  • Das I, Stein A, Kerle N, Dadhwal VK (2012) Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models. Geomorphology 179:116–125

    Article  Google Scholar 

  • Dempster AP (1968) Generalization of Bayesian inference. J R Stat Soc Series B 30:205–247

    Google Scholar 

  • Dou J, Yamagishi H, Pourghasemi HR, Song X, Ali YP, Xu Y, Zhu Z (2015) An integrated model for the landslide susceptibility assessment on Osado Island, Japan. Nat Hazards. doi:10.1007/s11069-015-1799-2

    Google Scholar 

  • Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41(6):720–730

    Article  Google Scholar 

  • Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Eng Geol 75:229

    Article  Google Scholar 

  • Esposito C, Barra A, Evans SG, Mugnozza GS, Delaney K (2014) Landslide susceptibility analysis by the comparison and integration of random forest and logistic regression methods; application to the disaster of Nova Friburgo-Rio de Janeiro, Brasil (January 2011). Geophys Res Abstr 16:11407

    Google Scholar 

  • Fiorucci F, Antonini G, Rossi M (2015) Implementation of landslide susceptibility in the Perugia Municipal Development Plan (PRG). In: Lollino et al. (eds) Engineering geology for society and territory, vol 5, pp 769–772

  • Geology Survey of Iran (GSI) (1997) http://www.gsi.ir/Main/Lang_en/index.html

  • Goetz JN, Guthrie RH, Brenning A (2011) Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology 129:376–386

    Article  Google Scholar 

  • Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11

    Article  Google Scholar 

  • Gorum T, Fan X, van Westen CJ, Huang RQ, Xu Q, Tang C, Wang G (2011) Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake. Geomorphology 133:152–167

    Article  Google Scholar 

  • Hasekiogullari 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(2):1157–1179

    Article  Google Scholar 

  • I.R. of Iran Meteorological Organization (2012) http://www.mazandaranmet.ir/

  • Iranian Landslide Working Party (ILWP) (2007) Iranian landslides list. Forest, Rangeland and Watershed Association, Iran, p 60

    Google Scholar 

  • Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11:909–926

    Article  Google Scholar 

  • Lawrence RL, Wood SD, Sheley RL (2006) Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (Random Forest). Remote Sens Environ 100:356–362

    Article  Google Scholar 

  • Lee S, Choi J (2004) Landslide susceptibility mapping using GIS and the weight-of evidence model. Int J Geogr Inf Sci 18(8):789–814

    Article  Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, using frequency ratio and logistic regression models. Landslides 4:33–41

    Article  Google Scholar 

  • Lee MJ, Choi JW, Oh HJ, Won JS, Park I, Lee S (2012) Ensemble-based landslide susceptibility maps in Jinbu area, Korea. Environ Earth Sci 67(1):23–37

    Article  Google Scholar 

  • Lee J, Lee K, Joung I, Joo K, Brooks BR, Lee J (2015a) Sigma-RF: prediction of the variability of spatial restraints in template-based modeling by random forest. BMC Bioinform. doi:10.1186/s12859-015-0526-z

    Google Scholar 

  • Lee MJ, Park I, Lee S (2015b) Forecasting and validation of landslide susceptibility using an integration of frequency ratio and neuro-fuzzy models: a case study of Seorak mountain area in Korea. Environ Earth Sci 74(1):413–429

    Article  Google Scholar 

  • Li XZ, Kong JM (2014) Application of GA–SVM method with parameter optimization for landslide development prediction. Nat Hazards Earth Syst Sci 14:525–533

    Article  Google Scholar 

  • Li C, Ma T, Sun L, Li W, Zheng A (2012) Application and verification of a fractal approach to landslide susceptibility mapping. Nat Hazards 61(1):169–185

    Article  Google Scholar 

  • Mallick J, Al-Wadi H, Atiqur Rahman, Ahmed M (2014) Landscape dynamic characteristics using satellite data from a mountainous watershed of Abha, Kingdom of Saudi Arabia. Environ Earth Sci. doi:10.1007/s12665-014-3408-1

    Google Scholar 

  • Martha TR, Kerle N, van Westen CJ, Jetten VG, Kumar KV (2012) Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories. ISPRS J Photogram Remote Sens 67:105–119

    Article  Google Scholar 

  • Meten M, Bhandary NP, Yatabe R (2015) Application of GIS-based fuzzy logic and rock engineering system (RES) approaches for landslide susceptibility mapping in Selelkula area of the Lower Jema River Gorge, Central Ethiopia. Environ Earth Sci 74(4):3395–3416

    Article  Google Scholar 

  • Meyer D, Leisch F, Hornik K (2003) The support vector machine under test. Neurocomputing 55(1–2):169–186

    Article  Google Scholar 

  • Micheletti N, Foresti L, Robert S, Leuenberger M, Pedrazzini A, Jaboyedoff M, Kanevski M (2014) Machine learning feature selection methods for landslide susceptibility mapping. Math Geosci 46:33–57

    Article  Google Scholar 

  • Mihir M, Malamud B, Rossi M, Reichenbach P, Ardizzone F (2014) Landslide susceptibility statistical methods: a critical and systematic literature review. Geophys Res Abstr 16(EGU2014-9814):2014

    Google Scholar 

  • Miner AS, Vamplew P, Windle DJ, Flentje P, Warner P (2010) A comparative study of various data mining techniques as applied to the modeling of landslide susceptibility on the Bellarine Peninsula, Victoria, Australia. Geologically active. In: Proceedings of the 11th IAEG congress of the international association of engineering geology and the environment, Auckland, New Zealand

  • 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 

  • Moosavi V, Niazi Y (2015) Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility mapping. Landslides. doi:10.1007/s10346-014-0547-0

    Google Scholar 

  • Naghibi SA, Pourghasemi HR, Pourtaghi ZS, Rezaei A (2015) Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan Watershed, Iran. Earth Sci Inform 8(1):171–186

    Article  Google Scholar 

  • Nampak H, Pradhan B, Manap MA (2014) Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol 513:283–300

    Article  Google Scholar 

  • Nandi A, Shakoor A (2010) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110:11–20

    Article  Google Scholar 

  • Nourani V, Pradhan B, Ghaffari H, Sharifi SS (2014) Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Nat Hazards 71(1):523–547

    Article  Google Scholar 

  • Oh HJ, Park NW, Lee SS, Lee S (2011a) Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping. Int J Remote Sens 33(10):3211–3231

    Article  Google Scholar 

  • Oh HJ, Kim YS, Choi JK et al (2011b) GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. J Hydrol 399:158–172

    Article  Google Scholar 

  • Osna T, Sezer EA, Akgun A (2014) GeoFIS: an integrated tool for the assessment of landslide susceptibility. Comput Geosci 66:20–30

    Article  Google Scholar 

  • Ozdemir A (2011a) Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J Hydrol 405:123–136

    Article  Google Scholar 

  • Ozdemir A (2011b) GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. J Hydrol 411:290–308

    Article  Google Scholar 

  • Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 64:180–197

    Article  Google Scholar 

  • Park NW (2014) Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets. Environ Earth Sci 73(3):937–949

    Article  Google Scholar 

  • Park I, Lee S (2014) Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area, Korea. Int J Remote Sens. doi:10.1080/01431161.2014.943326

    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(5):1443–1464

    Article  Google Scholar 

  • Poiraud A (2014) Landslide susceptibility-certainty mapping by a multi-method approach: a case study in the Tertiary basin of Puy-en-Velay (Massif central, France). Geomorphology. doi:10.1016/j.geomorph.2014.04.001

    Google Scholar 

  • Pourghasemi HR, Beheshtirad M (2014) Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran. Geocarto Int. doi:10.1080/10106049.2014.966161

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

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012b) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63(2):965–996

    Article  Google Scholar 

  • Pourghasemi HR, Moradi HR, Fatemi Aghda SM (2013) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69:749–779

    Article  Google Scholar 

  • Pourghasemi HR, Moradi HR, Fatemi Aghda SM (2014a) GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arab J Geosci 7(5):1857–1878

    Article  Google Scholar 

  • Pourghasemi HR, Moradi HR, Fatemi Aghda SM, Sezer EA, Goli Jirandeh A, Pradhan B (2014b) Assessment of fractal dimension and geometrical characteristics of landslides identified in North of Tehran, Iran. Environ Earth Sci 71:3617–3626

    Article  Google Scholar 

  • Pourtaghi Z, Pourghasemi HR, Rossi M (2014) Forest fire susceptibility mapping in the Minudasht Forests, Golestan Province, Iran. Environ Earth Scis. doi:10.1007/s12665-014-3502-4

    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(2):301–320

    Article  Google Scholar 

  • Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365

    Article  Google Scholar 

  • Pradhan B, Buchroithner MF (2010) Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environ Eng Geosci 16(2):107–126

  • Rahmati O, Pourghasemi HR, Melesse A (2016) Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region, Iran. Catena 137:360–372

    Article  Google Scholar 

  • Ren F, Wu X, Zhang K, Niu R (2014) Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China. Environ Earth Sci 73(8):4791–4804

    Article  Google Scholar 

  • Rodriguez-Galiano V, Chica-Rivas M (2012) Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and digital terrain models. Int J Digit Earth 7(6):1–18

    Google Scholar 

  • Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38(7):8208–8219

    Article  Google Scholar 

  • Shafer G (1976) A mathematical theory of evidence, vol 1. Princeton University, Princeton

    Google Scholar 

  • Shruthi RBV, Kerle N, Jetten VG, Stein A (2014) Object-based gully system prediction from medium resolution imagery using random forests. Geomorphology 216:283–294

    Article  Google Scholar 

  • Song Y, Gong J, Gao S, Wang D, Cui T, Li Y, Wei B (2012) Susceptibility assessment of earthquake induced landslides using Bayesian network: a case study in Beichuan, China. Comput Geosci 42:189–199

    Article  Google Scholar 

  • Stumpf A, Kerle N (2011) Object-oriented mapping of landslides using random forests. Remote Sens Environ 115:2564–2577

    Article  Google Scholar 

  • Su C, Wang L, Wang X, Huang Z, Zhang X (2015) Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine. Nat Hazards. doi:10.1007/s11069-014-1562-0

    Google Scholar 

  • Talebi A, Uijlenhoet R, Troch PA (2007) Soil moisture storage and hillslope stability. Nat Hazards Earth Syst Sci 7:523–534

    Article  Google Scholar 

  • Trigila A, Carla I, Carlo E, Gabriele SM (2015) Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology. doi:10.1016/j.geomorph.2015.06.001

    Google Scholar 

  • Umar Z, Pradhan B, Ahmad A, Jebur MN, Tehrany MS (2014) Earthquake-induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena 118:124–135

    Article  Google Scholar 

  • Varnes DJ (1978) Slope movement types and processes. In: Schuster RL, Krizek RJ (Eds) Landslides analysis and control. Special Report, vol. 176. Transportation Research Board, National Academy of Sciences, New York, pp 12–33

  • Vorpahl P, Elsenbeer H, Märker M, Schröder B (2012) How can statistical models help to determine driving factors of landslides? Ecol Model 239:27–39

    Article  Google Scholar 

  • Wang HB, Sassa K (2005) Comparative evaluation of landslide susceptibility in Minamata area, Japan. Environ Geol 47:956–966

    Article  Google Scholar 

  • Wang YT, Seijmonsbergen AC, Bouten W, Chen QT (2015) Using statistical learning algorithms in regional landslide susceptibility zonation with limited landslide field data. J Mount Sci. doi:10.1007/s11629-014-3134-x

    Google Scholar 

  • Watts JD, Lawrence RL, Miller PR, Montagne C (2009) Monitoring of cropland practices for carbon sequestration purposes in north central Montana by Landsat remote sensing. Remote Sens Environ 113:1843–1852

    Article  Google Scholar 

  • Weirich F, Blesius L (2007) Comparison of satellite and air photo based landslide susceptibility maps. Geomorphology 87(4):352–364

    Article  Google Scholar 

  • Williams G (2011) Data mining with Rattle and R. pp 245–268

  • Wu X, Niu R, Ren R, Peng L (2013) Landslide susceptibility mapping using rough sets and back-propagation neural networks in the Three Gorges, China. Environ Earth Sci 70(3):1307–1318

    Article  Google Scholar 

  • Wu XL, Ren F, Niu RQ (2014) Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China. Environ Earth Sci 71(11):4725–4738

    Article  Google Scholar 

  • Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey, PhD Thesis. Department of Geomatics the University of Melbourne, p 423

  • Youssef AM (2015) Landslide susceptibility delineation in the Ar-Rayth area, Jizan, Kingdom of Saudi Arabia, using analytical hierarchy process, frequency ratio, and logistic regression models. Environ Earth Sci 73(12):8499–8518

    Article  Google Scholar 

  • Youssef AM, Pradhan B, Jebur MN, El-Harbi HM (2014a) Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environ Earth Sci. doi:10.1007/s12665-014-3661-3

    Google Scholar 

  • Youssef AM, Al-kathery M, Pradhan B (2014b) Landslide susceptibility mapping at Al-Hasher Area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J. doi:10.1007/s12303-014-0032-8

    Google Scholar 

  • Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2015) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides. doi:10.1007/s10346-015-0614-1

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank four anonymous reviewers for their helpful comments on the primary version of the manuscript.

Author information

Authors and Affiliations

  1. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  2. Department of Earth Systems Analysis (ESA), Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands

    Norman Kerle

Authors
  1. Hamid Reza Pourghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Norman Kerle
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamid Reza Pourghasemi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pourghasemi, H.R., Kerle, N. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ Earth Sci 75, 185 (2016). https://doi.org/10.1007/s12665-015-4950-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-015-4950-1

Keywords

Search

Navigation