[go: up one dir, main page]
Skip to main content

Advertisement

SpringerLink
Log in

Shallow landslide susceptibility assessment using a novel hybrid intelligence approach

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

Abstract

We present a hybrid intelligent approach based on Naïve Bayes trees (NBT) and random subspace (RS) ensemble for landslide susceptibility mapping at the Bijar region, Kurdistan province (Iran). According to current literature, both NB and RS are machine learning techniques that have been rarely used for modeling of landslides. NBT is a relatively new decision trees-based algorithm in conjunction with Bayesian theories in building trees for classification, whereas RS is a relatively new ensemble framework with ability to improve performance of prediction models. In the hybrid approach, RS is used to generate subsets from the training data each subset is then used to construct a based classifier using NBT. For this purpose, a geospatial database for the study area was constructed that consisted of 111 landslide locations and 17 conditioning factors (slope degree, slope aspect, elevation above sea, curvature, profile curvature, plan curvature, stream power index, topographic wetness index, length-angle of slope, lithology, land use, distance to road, distance to fault, distance to stream, fault density, stream density, and rainfall). The database was used to construct and verify the proposed model. Performance of the model was evaluated using the receiver operating characteristics curve and area under the curve (AUC). The results showed that the proposed model performed well in this study (AUC = 0.886), and it improved significantly the performance of the NBT base classifier (AUC = 0.811). Overall, RS–NBT is promising which can be utilized for landslide susceptibility assessment in other landslide-prone areas.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  • Akgun A, Turk N (2011) Mapping erosion susceptibility by a multivariate statistical method: a case study from the AyvalIk region, NW Turkey. Comput Geosci 37:1515–1524. doi:10.1016/j.cageo.2010.09.006

    Article  Google Scholar 

  • Althuwaynee OF, Pradhan B, Lee S (2016) A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison. Int J Remote Sens 37:1190–1209

    Article  Google Scholar 

  • Atkinson P, Massari R (2011) Autologistic modelling of susceptibility to landsliding in the Central Apennines, Italy. Geomorphology 130:55–64

    Article  Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains. Cent Jpn Geomorphol 65:15–31

    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 

  • Beven K, Kirkby M (1979) A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol Sci J 24:43–69

    Google Scholar 

  • Bordoni M, Meisina C, Valentino R, Bittelli M, Chersich S (2015) Site-specific to local-scale shallow landslides triggering zones assessment using TRIGRS. Nat Hazards Earth Syst Sci 15(5):1025–1050

    Article  Google Scholar 

  • Bui DT, Pradhan B, Revhaug I, Nguyen DB, Pham HV, Bui QN (2015) 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) Geomatics. Nat Hazards Risk 6:243–271

    Article  Google Scholar 

  • Cevik E, Topal T (2003) GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol 44:949–962

    Article  Google Scholar 

  • Colkesen I, Sahin EK, Kavzoglu T (2016) Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. J Afr Earth Sc 118:53–64

    Article  Google Scholar 

  • Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 113:236–250

    Article  Google Scholar 

  • Cruden DM (1991) A simple definition of a landslide. Bull Eng Geol Environ 43:27–29

    Google Scholar 

  • Dehnavi A, Aghdam IN, Pradhan B, Varzandeh MHM (2015) A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena 135:122–148

    Article  Google Scholar 

  • Dong J-J, Tung Y-H, Chen C-C, Liao J-J, Pan Y-W (2009) Discriminant analysis of the geomorphic characteristics and stability of landslide dams. Geomorphology 110:162–171

    Article  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 41:720–730

    Article  Google Scholar 

  • Farrokhnia A, Pirasteh S, Pradhan B, Pourkermani M, Arian M (2011) A recent scenario of mass wasting and its impact on the transportation in Alborz Mountains, Iran using geo-information technology. Arab J Geosci 4:1337–1349. doi:10.1007/s12517-010-0238-7

    Article  Google Scholar 

  • Felicísimo ÁM, Cuartero A, Remondo J, Quirós E (2013) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10:175–189

    Article  Google Scholar 

  • Glade T, Anderson MG, Crozier MJ (2006) Landslide hazard and risk. Wiley, London

    Google Scholar 

  • Gorsevski PV, Jankowski P, Gessler PE (2005) Spatial prediction of landslide hazard using Fuzzy k-means and Dempster-Shafer theory. Trans GIS 9:455–474. doi:10.1111/j.1467-9671.2005.00229.x

    Article  Google Scholar 

  • Guo C, Montgomery DR, Zhang Y, Wang K, Yang Z (2015) Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology 248:93–110

    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 

  • Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184

    Article  Google Scholar 

  • He S, Pan P, Dai L, Wang H, Liu J (2012) Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China. Geomorphology 171:30–41

    Article  Google Scholar 

  • Hengl T, Gruber S, Shrestha D (2003) Digital terrain analysis in ILWIS International Institute for Geo-Information Science and Earth Observation Enschede, The Netherlands, p 62

  • Herath S, Wang Y (2009) Case studies and national experiences. In: Sassa K, Canuti P (eds) Landslides—Disaster Risk Reduction, Springer-Verlag, Berlin, Heidelberg, pp 475–497

  • Highland LM, Godt J, Howell D, Savage W (1998) El Nino 1997–98; damaging landslides in the San Francisco Bay area. US Department of the Interior, US Geological Survey, National Landslide Information Center

  • Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20:832–844

    Article  Google Scholar 

  • Hoang N-D, Tien Bui D (2016) A novel relevance vector machine classifier with cuckoo search optimization for spatial prediction of landslides. J Comput Civil Eng. doi:10.1061/(ASCE)CP.1943-5487.0000557

  • Hong H, Pradhan B, Xu C, Bui DT (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133:266–281. doi:10.16/j.catena.2015.05.019

    Article  Google Scholar 

  • Jordan M, Mitchell T (2015) Machine learning: trends, perspectives, and prospects. Science 349:255–260

    Article  Google Scholar 

  • Kjekstad O, Highland L (2009) Economic and social impacts of landslides. In: Zhou L, Ooi BC, Meng X (eds) Landslides–disaster risk reduction. Springer, Berlin, Heidelberg, pp 573–587

  • Kohavi R (1996) Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid. In: KDD. Citeseer, pp 202–207

  • Kritikos T, Davies T (2014) Assessment of rainfall-generated shallow landslide/debris-flow susceptibility and runout using a GIS-based approach: application to western Southern Alps of New Zealand. Landslides 12:1051–1075. doi:10.1007/s10346-014-0533-6

    Article  Google Scholar 

  • Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, London

    Book  Google Scholar 

  • Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174

    Article  Google Scholar 

  • Lee M-J, Choi J-W, Oh H-J, Won J-S, Park I, Lee S (2012) Ensemble based landslide susceptibility maps in Jinbu area. Korea Environ Earth Sci 67:23–37. doi:10.1007/s12665-011-1477-y

    Article  Google Scholar 

  • Li A, Khoo S, Lyamin A, Wang Y (2016) Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm. Autom Constr 65:42–50

    Article  Google Scholar 

  • Liu X, Lei J (2003) A method for assessing regional debris flow risk: an application in Zhaotong of Yunnan province (SW China). Geomorphology 52:181–191

    Article  Google Scholar 

  • Mao Y-M, Zhang M-S, Wang G-l, Sun P-P (2015) Landslide hazards mapping using uncertain Naïve Bayesian classification method. J Central South Univ 22:3512–3520

    Article  Google Scholar 

  • Moore ID, Burch GJ (1986) Physical basis of the length-slope factor in the Universal Soil Loss Equation. Soil Sci Soc Am J 50:1294–1298

    Article  Google Scholar 

  • Moore ID, Wilson JP (1992) Length-slope factors for the revised universal soil loss equation: simplified method of estimation. J Soil Water Conserv 47:423–428

    Google Scholar 

  • Mousavi SZ, Kavian A, Soleimani K, Mousavi SR, Shirzadi A (2011) GIS-based spatial prediction of landslide susceptibility using logistic regression model. Geomat Nat Hazards Risk 2:33–50

    Article  Google Scholar 

  • Oh H-J, Lee S (2011) Landslide susceptibility mapping on Panaon Island, Philippines using a geographic information system. Environ Earth Sci 62:935–951. doi:10.1007/s12665-010-0579-2

    Article  Google Scholar 

  • Oh H-J, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37:1264–1276. doi:10.1016/j.cageo.2010.10.012

    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 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 35:6089–6112

    Article  Google Scholar 

  • Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia MB (2015) Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol 122:1–19. doi:10.1007/s00704-015-1702-9

    Article  Google Scholar 

  • Pham BT, Bui DT, Dholakia M, Prakash I, Pham HV (2016a) A comparative study of least square support vector machines and multiclass alternating decision trees for spatial prediction of rainfall-induced landslides in a Tropical cyclones area. Geotech Geol Eng. doi:10.1007/s10706-016-9990-0:1-18

    Google Scholar 

  • Pham BT, Tien Bui D, Prakash I, Dholakia MB (2016b) Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS. Nat Hazards. doi:10.1007/s11069-016-2304-2

    Google Scholar 

  • Pham BT, Bui DT, Prakash I, Dholakia M (2017) Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149:52–63

    Article  Google Scholar 

  • Poudyal CP, Chang C, Oh H-J, Lee S (2010) Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environ Earth Sci 61:1049–1064

    Article  Google Scholar 

  • Pradhan AMS, Kim Y-T (2014) Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek. South Korea Nat Hazards 72:1189–1217

    Article  Google Scholar 

  • Pradhan B, Lee S (2010) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland. Malays Landslides 7:13–30. doi:10.1007/s10346-009-0183-2

    Article  Google Scholar 

  • Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia). IEEE Trans Geosci Remote Sens 48:4164–4177. doi:10.1109/tgrs.2010.2050328

    Article  Google Scholar 

  • Pradhan B, Abokharima MH, Jebur MN, Tehrany MS (2014) Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards 73:1019–1042

    Article  Google Scholar 

  • Saha A, Gupta R, Arora M (2002) GIS-based landslide hazard zonation in the Bhagirathi (Ganga) valley, Himalayas. Int J Remote Sens 23:357–369

    Article  Google Scholar 

  • Sarkar S, Kanungo DP, Patra A, Kumar P (2008) GIS based spatial data analysis for landslide susceptibility mapping. J Mt Sci 5:52–62

    Article  Google Scholar 

  • 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 

  • Sidle RC, Ochiai H (2006) Landslides: processes, prediction, and land use, vol 18. American Geophysical Union, Washington

    Book  Google Scholar 

  • Skurichina M, Duin RP (2002) Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal Appl 5:121–135

    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 

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

    Article  Google Scholar 

  • Tehrany MS, Pradhan B, Jebur MN (2014) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343

    Article  Google Scholar 

  • Tianchi L, Shumin W (1992) Landslide hazards and their mitigation in China. Science Press, Beijing

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012a) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree and Naïve Bayes models. Math Probl Eng 2012:1–26

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211. doi:10.1016/j.cageo.2011.10.031

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Revhaug I, Trung Tran C (2014) A Comparative Assessment Between the Application of Fuzzy Unordered Rules Induction Algorithm and J48 Decision Tree Models in Spatial Prediction of Shallow Landslides at Lang Son City, Vietnam. In: Srivastava PK, Mukherjee S, Gupta M, Islam T (eds) Remote sensing applications in environmental research. Society of Earth Scientists Series. Springer International Publishing, Cham, Switzerland, pp 87–111. doi:10.1007/978-3-319-05906-8_6

  • Tien Bui D, Anh Tuan T, Hoang N-D, Quoc Thanh N, Nguyen BD, Van Liem N, Pradhan B (2016a) Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a novel hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony. Optim Landslides. doi:10.1007/s10346-016-0711-9

    Google Scholar 

  • Tien Bui D, Nguyen Q-P, Hoang N-D, Klempe H (2016b) A novel fuzzy K-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS. Landslides. doi:10.1007/s10346-016-0708-4

    Google Scholar 

  • Tien Bui D, Pham TB, Nguyen Q-P, Hoang N-D (2016c) Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of least squares support vector machines and differential evolution optimization: a case study in central Vietnam. Int J Digital Earth. doi:10.1080/1753894720161169561

    Google Scholar 

  • Trigila A, Iadanza C, Esposito C, Scarascia-Mugnozza G (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 

  • Tsangaratos P, Ilia I (2015) Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece. Landslides. doi:10.1007/s10346-015-0565-6

    Google Scholar 

  • Tsangaratos P, Ilia I (2016) Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size. CATENA 145:164–179

    Article  Google Scholar 

  • Uromeihy A, Mahdavifar M (2000) Landslide hazard zonation of the Khorshrostam area. Iran Bulletin of Engineering Geology and the Environment 58:207–213

    Article  Google Scholar 

  • Van Westen C, Bonilla J (1990) Mountain hazard analysis using PC-based GIS. In: 6th IAEG Congress, pp 265–271

  • Vasu NN, Lee S-R (2016) A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt, Woomyeon, South Korea. Geomorphology 263:50–70

    Article  Google Scholar 

  • Wang L-J, Sawada K, Moriguchi S (2013) Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy. Comput Geosci 57:81–92

    Article  Google Scholar 

  • Wang L-J, Guo M, Sawada K, Lin J, Zhang J (2015) Landslide susceptibility mapping in Mizunami City, Japan: a comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models. Catena 135:271–282

    Article  Google Scholar 

  • Wilson JP, Gallant JC (2000) Terrain analysis: principles and applications. Wiley, London

    Google Scholar 

  • Xu C, Xu X, Dai F, Saraf AK (2012) Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Comput Geosci 46:317–329

    Article  Google Scholar 

  • 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 

  • 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, pp 1–18

  • Zare M, Pourghasemi HR, Vafakhah M, Pradhan B (2013) Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci 6:2873–2888

    Article  Google Scholar 

  • Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39:561–577

    Google Scholar 

Download references

Acknowledgements

The authors wish to thank the Forests, Rangelands, and Watershed Management Organization of Iran for preparing the report of landslide location in the study area, University of Kurdistan and University of Agricultural and Natural Resources of Sari for their financial supports. The authors would also like to thank the anonymous reviewers and editors for their valuable and constructive comments on the earlier version of the manuscript.

Author information

Authors and Affiliations

  1. Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

    Ataollah Shirzadi & Kamran Chapi

  2. Geographic Information System Group, Department of Business Administration and Computer Science, University College of Southeast Norway, 3800, Bø i Telemark, Norway

    Dieu Tien Bui

  3. Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Viet Nam

    Binh Thai Pham

  4. Department of Watershed Sciences Engineering, College of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari, Iran

    Karim Solaimani & Ataollah Kavian

  5. Departments of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

    Himan Shahabi

  6. Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003 IMT, 1432, Ås, Norway

    Inge Revhaug

Authors
  1. Ataollah Shirzadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dieu Tien Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Binh Thai Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Karim Solaimani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kamran Chapi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ataollah Kavian
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Himan Shahabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Inge Revhaug
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kamran Chapi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shirzadi, A., Bui, D.T., Pham, B.T. et al. Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environ Earth Sci 76, 60 (2017). https://doi.org/10.1007/s12665-016-6374-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-016-6374-y

Keywords

Search

Navigation