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Terrigenous Mass Movements pp 193–220Cite as

Ensemble-Based Landslide Susceptibility Maps in Jinbu Area, Korea

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Abstract

Ensemble techniques were developed, applied and validated for analysis of landslide susceptibility in Jinbu area, Korea using the geographic information system (GIS). Landslide occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 70/30 ratio for training and validation of the models, respectively. Topography, geology, soil and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. By using the constructed spatial database, 17 landslide-related factors were extracted. The relationships between the detected landslide locations and the factors were identified and quantified by frequency ratio, weight of evidence, logistic regression and artificial neural network models and their ensemble models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. Then, the four landslide susceptibility maps were used as new input factors and integrated using the frequency ratio, weight of evidence, logistic regression and artificial neural network models as ensemble methods to make better susceptibility maps. All of the susceptibility maps were validated by comparison with known landslide locations that were not used directly in the analysis. As the result, the ensemble-based landslide susceptibility map that used the new landslide-related input factor maps showed better accuracy (87.11% in frequency ratio, 83.14% in weight of evidence, 87.79% in logistic regression and 84.54% in artificial neural network) than the individual landslide susceptibility maps (84.94% in frequency ratio, 82.82% in weight of evidence, 87.72% in logistic regression and 81.44% in artificial neural network). All accuracy assessments showed overall satisfactory agreement of more than 80%. The ensemble model was found to be more effective in terms of prediction accuracy than the individual model.

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References

  • Akgun A, Bulut F (2009) GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Env Geol 9:339–404

    Google Scholar 

  • Akgun A, Türk N (2010) Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis. Environ Earth Sci 61:595–611

    Article  Google Scholar 

  • Audisio C, Nigrelli G, Lollino G (2009) A GIS tool for historical instability processes data entry: an approach to hazard management in two Italian Alpine river basins. Comput Geosci 35:1735–1747

    Article  Google Scholar 

  • Bai S, Lü G, Wang J, Zhou P, Ding L (2011) GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environ Earth Sci 62:139–149

    Article  Google Scholar 

  • Bai S, Wang J, Lu GN, Zhou PG, Hou SS, Xu SN (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115:23–31

    Article  Google Scholar 

  • Beven KJ, Kirkby MJ (1979) Physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull des Sci Hydrologiques 24:43–69

    Article  Google Scholar 

  • Bonham-Carter GF (1994) Geographic information systems for geoscientists, modeling with GIS. Pergamon Press, Oxford

    Google Scholar 

  • Bonham-Carter GF, Agterberg FP, Wright DF (1989) Weights of evidence modeling: a new approach to mapping mineral potential. In: Agterberg FP, Bonham-Carter GF (eds) Statistical applications in the Earth sciences. Geological Survey of Canada, Ottawa, pp 171–183

    Google Scholar 

  • Brabb EE (1984) Innovative approaches to landslide hazard and risk mapping. In: Proceedings, fourth international symposium on landslides, vol 1. Canadian Geotechnical Society, Toronto, pp 307–324

    Google Scholar 

  • Chauhan S, Sharma M, Arora MK (2010) Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides 7:411–423

    Article  Google Scholar 

  • Chen CH, Ke CC, Wang CL (2009a) A back-propagation network for the assessment of susceptibility to rock slope failure in the eastern portion of the Southern Cross-Island Highway in Taiwan. Env Geol 57:723–733

    Article  Google Scholar 

  • Chen YR, Chen JW, Hsieh SC, Ni PN (2009b) The application of remote sensing technology to the interpretation of land use for rainfall-induced landslides based on genetic algorithms and artificial neural networks. IEEE J Sel Top Appl Earth Observations Remote Sens 2:87–95

    Article  Google Scholar 

  • Clerici A, Perego S, Tellini C, Vescovi P (2002) A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology 48:349–364

    Article  Google Scholar 

  • Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Takuro M, Nishino K (2008) GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Env Geol 54:311–324

    Article  Google Scholar 

  • Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228

    Article  Google Scholar 

  • Das I, Sahoo S, van Westen C, Stein A, Hack R (2010) Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology 114:627–637

    Article  Google Scholar 

  • Dominguez-Cuesta MJ, Jimenez-Sanchez M, Colubi A, Gonzalez-Rodriguez G (2010) Modelling shallow landslide susceptibility: a new approach in logistic regression by using favourability assessment. Int J Earth Sci 99:661–674

    Article  Google Scholar 

  • Dong JJ, Lee CT, Tung YH, Liu CN, Lin KP, Lee JF (2009) The role of the sediment budget in understanding debris flow susceptibility. Earth Surf Process Landform 34:1612–1624

    Article  Google Scholar 

  • Falaschi F, Giacomelli F, Federici PR, Puccinelli A, D’Amato Avanzi G, Pochini A, Ribolini A (2009) Logistic regression versus artificial neural networks: Landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy. Nat Hazards 50:551–569

    Article  Google Scholar 

  • Garrett J (1994) Where and why artificial neural networks are applicable in civil engineering. J Comput Civil Eng 8:129–130

    Article  Google Scholar 

  • Geological Society of Korea (1962) Changdong-Hajinburi geological map sheet

    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 

  • Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72:272–299

    Article  Google Scholar 

  • He YR, Fu WJ (2009) Application of fuzzy support vector machine to landslide risk assessment. J Nat Disasters 18:107–112

    Google Scholar 

  • Hines JW (1997) Fuzzy and neural approaches in engineering. Wiley, New York

    Google Scholar 

  • Kanungo DP, Arora MK, Gupta RP, Sarkar S (2008) Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Himalayas. Landslides 5:407–416

    Article  Google Scholar 

  • Kanungo DP, Arora MK, Sarkar S, Gupta RP (2009) A fuzzy set based approach for integration of thematic maps for landslide susceptibility zonation. Georisk 3:30–43

    Google Scholar 

  • Kawabata D, Bandibas J (2009) Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN). Geomorphology 113:97–109

    Article  Google Scholar 

  • Lee DS (1988) Geology of Korea (second ed.), Kyohak-Sa, Seoul

    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 (2007a) Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surf Proc Land 32:2133–2148

    Article  Google Scholar 

  • Lee S (2007b) Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Env Geol 52:615–623

    Article  Google Scholar 

  • Lee S, Choi J, Woo I (2004a) The effect of spatial resolution on the accuracy of landslide susceptibility mapping: A case study in Boun, Korea. Geosci J 8:51–60

    Article  Google Scholar 

  • Lee S, Evangelista DG (2006) Earthquake-induced landslide-susceptibility mapping using an artificial neural network. Nat Hazards Earth Syst Sci 6:687–695

    Article  Google Scholar 

  • Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Youngin, Korea. Env Geol 40:1095–1113

    Article  Google Scholar 

  • Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Syst Sci 115:661–672

    Article  Google Scholar 

  • Lee S, Ryu JH, Lee MJ, Won JS (2006) The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea. Math Geol 38:199–220

    Article  Google Scholar 

  • Lee S, Ryu JH, Won JS, Park HJ (2004b) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71:289–302

    Article  Google Scholar 

  • Lee S, Song KY, Oh HJ, Choi J (2011) Detection of landslide using web-based aerial photographs and landslide susceptibility mapping using geospatial analysis. Int J Remote Sensing, Accepted 7 Jun 2011

    Google Scholar 

  • Legorreta Paulin G, Bursik M (2009) Logisnet: A tool for multimethod, multiple soil layers slope stability analysis. Comput Geosci 35:1007–1016

    Article  Google Scholar 

  • Liu CN, Dong JJ, Peng YF, Huang HF (2009) Effects of strong ground motion on the susceptibility of gully type debris flows. Eng Geol 104:241–253

    Article  Google Scholar 

  • Mathew J, Jha VK, Rawat GS (2009) Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides 6:17–26

    Article  Google Scholar 

  • Melchiorre C, Matteucci M, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94:379–400

    Article  Google Scholar 

  • Miles SB, Keefer DK (2009) Evaluation of CAMEL–comprehensive areal model of earthquake-induced landslides. Eng Geol 104:1–15

    Article  Google Scholar 

  • Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30

    Article  Google Scholar 

  • Mousavi SR, Pirasteh S, Shattri M, Amani A (2009) Landslides and Active Faults Using Remote Sensing and GIS Techniques in Central Alborz Mountains, Iran. Disaster Adv 2(3):24–29

    Google Scholar 

  • Muthu K, Petrou M, Tarantino C, Blonda P (2008) Landslide possibility mapping using fuzzy approaches. IEEE Trans Geosci Remote Sensing 46:1253–1265

    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 

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

    Article  Google Scholar 

  • Oh HJ, Lee S (2010) Cross-validation of logistic regression model for landslide susceptibility mapping at Geneoung areas, Korea. Disaster Adv 3(2):44–55

    Google Scholar 

  • Ozdemir A (2009) Landslide susceptibility mapping of vicinity of Yaka Landslide (Gelendost, Turkey) using conditional probability approach in GIS. Env Geol 57:1675–1686

    Article  Google Scholar 

  • Park NW (2011) Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environ Earth Sci 62:367–376

    Article  Google Scholar 

  • Pirasteh S, Pradhan B, Mahmoodzadeh A (2009) Stability mapping and landslide recognition in zagros mountain South West Iran: a case study. Disaster Adv 2(1):47–53

    Google Scholar 

  • Poudyal CP, Chang C, Oh HJ, 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 

  • Prabu S, Ramakrishnan SS (2009) Combined use of socio economic analysis, remote sensing and GIS data for landslide hazard mapping using ANN. J Indian Soc Remote Sensing 37:409–421

    Article  Google Scholar 

  • Pradhan B, Lee S (2007) Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis by using an artificial neural network model at Selangor, Malaysia. Earth Sci Front 14:143–151

    Article  Google Scholar 

  • Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focussing on different training sites. Int J Phys Sci 4:1–15

    Google Scholar 

  • Pradhan B, Lee S, Buchroithner MF (2010) A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Comput Environ Urban Syst 34:216–235

    Article  Google Scholar 

  • Pradhan B, Lee S (2010a) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60:1037–1054

    Article  Google Scholar 

  • Pradhan B, Lee S (2010b) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25:747–759

    Article  Google Scholar 

  • Pradhan B, Lee S (2010c) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7:13–30

    Article  Google Scholar 

  • Regmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115:172–187

    Article  Google Scholar 

  • Rokach L (2005) Ensemble methods for classifiers. In: Maimon O, Rokach L (eds) The data mining and knowledge discovery handbook. Springer, Berlin, pp 957–958

    Chapter  Google Scholar 

  • Shafri HZM, Zahidi IM, Bakar SA (2010) Development of landslide susceptibility map utilizing remote sensing and Geographic Information Systems (GIS). Disaster Prev Manage 19:59–69

    Article  Google Scholar 

  • Tangestani MH (2009) A comparative study of Dempster-Shafer and fuzzy models for landslide susceptibility mapping using a GIS: an experience from Zagros Mountains, SW Iran. J Asian Earth Sci 35:66–73

    Article  Google Scholar 

  • Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2009) Landslide hazard zonation using quantitative methods in GIS. Int J Civil Eng 7:176–189

    Google Scholar 

  • Wang WD, Xie CM, Du XG (2009) Landslides susceptibility mapping based on geographical information system, GuiZhou, south-west China. Env Geol 58:33–43

    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 

  • Yilmaz I (2009a) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bull Eng Geol Environ 68:297–306

    Article  Google Scholar 

  • Yilmaz I (2009b) GIS based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar, Turkey). Bull Eng Geol Environ 68:459–471

    Article  Google Scholar 

  • Yilmaz I (2009c) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Comput Geosci 35:1125–1138

    Article  Google Scholar 

  • Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 68:821–836

    Article  Google Scholar 

  • Zhou W (1999) Verification of the nonparametric characteristics of backpropagation neural networks for image classification. IEEE T Geosci Remote 37:771–779

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Knowledge and Economy of Korea.

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Authors and Affiliations

  1. Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 92 Gwahang-no, Yuseong-gu, Daejeon, 305-350, Korea

    Saro Lee

  2. Mineral Resources Research Department, Korea Institute of Geoscience and Mineral Resources (KIGAM), 92 Gwahang-no, Yuseong-gu, Daejeon, 305-350, Korea

    Hyun-Joo Oh

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  1. Saro Lee
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Correspondence to Saro Lee .

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Editors and Affiliations

  1. , Institute of Advanced Technology Spatial, University Putra Malaysia (UPM) Serdang, Selangor Darul Ehsan, 43400, Malaysia

    Biswajeet Pradhan

  2. TU Dresden, Mommsenstr. 23, Dresden, 01062, Germany

    Manfred Buchroithner

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Lee, S., Oh, HJ. (2012). Ensemble-Based Landslide Susceptibility Maps in Jinbu Area, Korea. In: Pradhan, B., Buchroithner, M. (eds) Terrigenous Mass Movements. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25495-6_7

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