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An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan

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Abstract

The objective of this study was to select the maximum number of correlated factors with landslide occurrence for slope-instability mapping and assess landslide susceptibility on Osado Island, Niigata Prefecture, Central Japan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN), in a geographic information system (GIS) environment. The landslide inventory data of the National Research Institute for Earth Science and Disaster Prevention (NIED) and a 10-m digital elevation model (DEM) from the Geographical Survey of Institute, Japan, were analyzed. Our study identified fourteen possible landslide-conditioning factors. Considering the spatial autocorrelation and factor redundancy, we applied the CF approach to optimize these set of variables. We hypothesize that if the thematic factor layers of the CF values are positive, it implies that these conditioning factors have a correlation with the landslide occurrence. Therefore, based on this assumption and because of their positive CF values, six conditioning factors including slope angle (0.04), slope aspect (0.02), drainage density network (0.34), distance to the geologic boundaries (0.37), distance to fault (0.35), and lithology (0.31) have been selected as landslide-conditioning factors for further analysis. We partitioned the data into two groups: 70 % (520 landslide locations) for model training and the remaining 30 % (220 landslide locations) for validation. Then, a common ANN model, namely the back-propagation neural network (BPNN), was employed to produce the landslide susceptibility maps. The receiver operating characteristics including the area under the curve (AUC) were used to assess the model accuracy. The validation results indicate that the values of the AUC at optimized and non-optimized BPNN were 0.82 and 0.73, respectively. Hence, it is concluded that the optimized factor model can provide superior accuracy in the prediction of landslide susceptibility in the study area. In this context, we propose a method to select the factors with landslide occurrence. This work is fundamental for further study of the landslide susceptibility evaluation and prediction.

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References

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44. doi:10.1007/s100640050066

    Article  Google Scholar 

  • Arora MK, Das Gupta AS, Gupta RP (2004) An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int J Remote Sens 25:559–572. doi:10.1080/0143116031000156819

    Article  Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31. doi:10.1016/j.geomorph.2004.06.010

    Article  Google Scholar 

  • Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: part I. Case studies, monitoring techniques and environmental considerations. Eng Geol 81:419–431

    Article  Google Scholar 

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

    Google Scholar 

  • Binaghi E, Luzi L, Madella P et al (1998) Slope instability zonation: a comparison between certainty factor and fuzzy Dempster–Shafer approaches. Nat Hazards 17:77–97. doi:10.1023/A:1008001724538

    Article  Google Scholar 

  • Brabb EE (1984) Innovative approaches to landslide hazard mapping. Proc 4th Int Symp Landslides 1:307–324

    Google Scholar 

  • Chang T-C, Chao R-J (2006) Application of back-propagation networks in debris flow prediction. Eng Geol 85:270–280. doi:10.1016/j.enggeo.2006.02.007

    Article  Google Scholar 

  • Chauhan S, Sharma M, Arora MK, Gupta NK (2010) Landslide susceptibility zonation through ratings derived from artificial neural network. Int J Appl Earth Obs Geoinf 12:340–350. doi:10.1016/j.jag.2010.04.006

    Article  Google Scholar 

  • Chung C-J, Fabbri AG (1993) The representation of geoscience information for data integration. Nonrenewable Resour 2:122–139. doi:10.1007/BF02272809

    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. doi:10.1016/j.catena.2013.08.006

    Article  Google Scholar 

  • Dou J, Qian J, Zhang H et al (2009) Landslides detection: a case study in Conghua city of Pearl River delta. Second Int Conf Earth Obs Glob Chang. doi:10.1117/12.836328

    Google Scholar 

  • Dou J, Oguchi T, Hayakawa YS et al (2014) Susceptibility mapping using a certainty factor model and its validation in the Chuetsu area, Central Japan. Landslide Sci Safer Geoenviron 2:483–489. doi:10.1007/978-3-319-05050-8_65

    Google Scholar 

  • Dou J, Chang K, Chen S et al (2015a) Automatic case-based reasoning approach for landslide detection: integration of object-oriented image analysis and a genetic algorithm. Remote Sens. doi:10.3390/rs70404318

    Google Scholar 

  • Dou J, Li X, Yunus AP et al (2015b) Automatic detection of sinkhole collapses at finer resolutions using a multi-component remote sensing approach. Nat Hazards 26:1–24. doi:10.1007/s11069-015-1756-0

    Google Scholar 

  • Dou J, Paudel U, Oguchi T et al (2015c) Differentiation of shallow and deep-seated landslides using support vector machines: a case study of the Chuetsu area, Japan. Terr Atmos Ocean Sci 26:227–239. doi:10.3319/TAO.2014.12.02.07(EOSI)

    Article  Google Scholar 

  • Falaschi F, Giacomelli F, Federici PR et al (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 

  • Farrokhzad F, Barari A, Choobbasti AJ, Ibsen LB (2011) Neural network-based model for landslide susceptibility and soil longitudinal profile analyses: two case studies. J African Earth Sci 61:349–357. doi:10.1016/j.jafrearsci.2011.09.004

    Article  Google Scholar 

  • Feizizadeh B, Jankowski P, Blaschke T (2014) A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis. Comput Geosci 64:81–95. doi:10.1016/j.cageo.2013.11.009

    Article  Google Scholar 

  • Gessler PE, Moore ID, McKenzie NJ, Ryan PJ (1995) Soil-landscape modelling and spatial prediction of soil attributes. Int J Geogr Inf Syst 9:421–432

    Article  Google Scholar 

  • Gökceoglu C, Aksoy H (1996) susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Eng Geol 44:147–161

    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. doi:10.1016/S0169-555x(99)00078-1

    Article  Google Scholar 

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  • Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85:347–366. doi:10.1016/j.enggeo.2006.03.004

    Article  Google Scholar 

  • Kanungo DP, Sarkar S, Sharma S (2011) Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides. Nat Hazards 59:1491–1512. doi:10.1007/s11069-011-9847-z

    Article  Google Scholar 

  • Klimes J (2013) Landslide temporal analysis and susceptibility assessment as bases for landslide mitigation, Machu Picchu, Peru. Environ Earth Sci 70:913–925. doi:10.1007/s12665-012-2181-2

    Article  Google Scholar 

  • Li Y, Chen G, Tang C et al (2012) Rainfall and earthquake-induced landslide susceptibility assessment using GIS and artificial neural network. Nat Hazards Earth Syst Sci 12:2719–2729. doi:10.5194/nhess-12-2719-2012

    Article  Google Scholar 

  • Miliaresis G, Kokkas N (2007) Segmentation and object-based classification for the extraction of the building class from LIDAR DEMs. Comput Geosci 33:1076–1087. doi:10.1016/j.cageo.2006.11.012

    Article  Google Scholar 

  • Nefeslioglu HA, Sezer EA, Gokceoglu C, Ayas Z (2013) A modified analytical hierarchy process (M-AHP) approach for decision support systems in natural hazard assessments. Comput Geosci 59:1–8. doi:10.1016/j.cageo.2013.05.010

    Article  Google Scholar 

  • Oguchi T (1997) Drainage density and relative relief in humid steep mountains with frequent slope failure. Earth Surf Process Landforms 22:107–120

    Article  Google Scholar 

  • Park S, Choi C, Kim B, Kim J (2012) 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. doi:10.1007/s12665-012-1842-5

    Article  Google Scholar 

  • Pavel M, Nelson JD, Jonathan Fannin R (2011) An analysis of landslide susceptibility zonation using a subjective geomorphic mapping and existing landslides. Comput Geosci 37:554–566. doi:10.1016/j.cageo.2010.10.006

    Article  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. doi:10.1016/j.catena.2012.05.005

    Article  Google Scholar 

  • Pourghasemi H, Pradhan B, Gokceoglu C, Moezzi KD (2013) A comparative assessment of prediction capabilities of Dempster–Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomatics Nat Hazards Risk 4:93–118. doi:10.1080/19475705.2012.662915

    Article  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. doi:10.1016/j.compenvurbsys.2009.12.004

    Article  Google Scholar 

  • Prasad R, Pandey A, Singh KP et al (2012) Retrieval of spinach crop parameters by microwave remote sensing with back propagation artificial neural networks: a comparison of different transfer functions. Adv Sp Res 50:363–370. doi:10.1016/j.asr.2012.04.010

    Article  Google Scholar 

  • Rumelhart D, Hinton G, Williams R (1986) Learning representations by back-propagating errors. Nature 323:533–536

    Article  Google Scholar 

  • Shi H-Y, Lee K-T, Lee H-H et al (2012) Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery. PLoS ONE 7:e35781. doi:10.1371/journal.pone.0035781

    Article  Google Scholar 

  • Shortliffe EH (1976) Computer-based medical consultations: MYCIN. North-Holland, New York

    Google Scholar 

  • Song JH, Venkatesh SS, Conant EA et al (2005) Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses. Acad Radiol 12:487–495. doi:10.1016/j.acra.2004.12.016

    Article  Google Scholar 

  • Sumer E, Turker M (2013) An adaptive fuzzy-genetic algorithm approach for building detection using high-resolution satellite images. Comput Environ Urban Syst 39:48–62. doi:10.1016/j.compenvurbsys.2013.01.004

    Article  Google Scholar 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O et al (2012) Landslide susceptibility assessment in the Hoa Binh province of Vietnam: a comparison of the Levenberg–Marquardt and Bayesian regularized neural networks. Geomorphology 171–172:12–29. doi:10.1016/j.geomorph.2012.04.023

    Article  Google Scholar 

  • Turner AK, Schuster R (1996) Landslides: investigation and mitigation. Transp. Res. Board, Washington, DC

    Google Scholar 

  • Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2010) A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci 36:1101–1114. doi:10.1016/j.cageo.2010.04.004

    Article  Google Scholar 

  • Van Westen CJ, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation. Geol Rundschau 86:404–414. doi:10.1007/s005310050149

    Article  Google Scholar 

  • Yamagishi H (2008) GIS mapping of landscape and disasters of Sado Island, Japan. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVII:1429–1432 (Part B7, Beijing 2008)

  • 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. doi:10.1016/j.enggeo.2005.02.002

    Article  Google Scholar 

  • Yilmaz I (2009) 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. doi:10.1016/j.cageo.2008.08.007

    Article  Google Scholar 

  • Youssef AM, Pradhan B, Pourghasemi HR, Abdullahi S (2015) Landslide susceptibility assessment at Wadi Jawrah Basin, Jizan region, Saudi Arabia using two bivariate models in GIS. Geosci J doi:10.1007/s12303-014-0065-z

  • 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. doi:10.1007/s12517-012-0610-x

    Article  Google Scholar 

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Acknowledgments

We would like to express our gratitude to Midori NET Niigata and Sado City for providing the ortho photographs of Sado Island and the NIED for providing the landslide data.

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Authors and Affiliations

  1. Department of Natural Environmental Studies, The University of Tokyo, Kashiwa, 277-8568, Japan

    Jie Dou & Ali P. Yunus

  2. Asian Institute of Space Information, Shiroishi-ku, Hngo-dori 2-choume kita 3-10, Sapporo, 003-0025, Japan

    Hiromitsu Yamagishi

  3. Department of Natural Resources and Environment, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  4. Center for Spatial Information Science, The University of Tokyo, 5-1-5, Kashiwa, 277-8568, Japan

    Xuan Song

  5. Institute of Earthquake Science, China Earthquake Administration, Fuxing Rd. 36, Beijing, China

    Yueren Xu

  6. College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing, 100875, China

    Zhongfan Zhu

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  1. Jie Dou
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  2. Hiromitsu Yamagishi
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  4. Ali P. Yunus
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  5. Xuan Song
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Correspondence to Jie Dou.

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Dou, J., Yamagishi, H., Pourghasemi, H.R. et al. An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Nat Hazards 78, 1749–1776 (2015). https://doi.org/10.1007/s11069-015-1799-2

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