Abstract
In this study, a new ensemble method was developed to assess landslide hazard models in Mt. Umyeon, South Korea, using the results of a physically based model as a conditioning factor (CF). Hydrological conditions were obtained from the national-scale rainfall threshold. To incorporate rainfall threshold in landslide initiation, national landslide inventory data were used to prepare I-D and C-D thresholds. A series of factor of safety (FS) distribution maps were prepared using a physically based model with a 12-h cumulative rainfall threshold. We created an ensemble model to overcome limitations in the physically based model, which could not incorporate important environmental variables such as hydrology, forest, soil, and geology. To determine the effect of CFs on landslide distribution, spatial data layers of elevation, drainage proximity, soil drainage characters, stream power index, sediment transport index, topographic wetness index, forest type, forest density, tree diameter, soil type geology, and the FS distribution map were analyzed in a maximum entropy-based machine learning algorithm. Validation was performed with a receiver operating characteristic curve (ROC). The ROC showed 65.9% accuracy in the physically based model, whereas the ensemble model had higher accuracy (79.6%) and a prediction rate of 89.7%. The ensemble landslide hazard model is a new approach, incorporating the FS distribution map into the available independent environmental variables.
Similar content being viewed by others
References
Akgün 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(1):23–34
Aleotti P (2003) A warning system for rainfall-induced shallow failures. Eng Geol 73(3):247–265
Baum RL, Godt JW (2010) Early warning of rainfall-induced shallow landslides and debris flows in the USA. Landslides 7(3):259–272
Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci J 24(1):43–69
Brunetti MT, Peruccacci S, Rossi M, Luciani S, Valigi D, Guzzetti F (2010) Rainfall thresholds for the possible occurrence of landslides in Italy. Nat Hazards Earth Syst Sci 10(3):447–458
Brunsden D, Prior DB (1984) Slope stability. Wiley, New York, p 620
Caine N (1980) The rainfall intensity: duration control of shallow landslides and debris flows. Geogr Ann Ser B:23–27
Caniani D, Pascale S, Sdao F, Sole A (2008) Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Nat Hazards 45(1):55–72
Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1995) GIS technology in mapping landslide hazard. In: Geographical information systems in assessing natural hazards. Springer, Netherlands, p 135–175
Chen CY, Chen TC, Yu FC, Yu WH, Tseng CC (2005) Rainfall duration and debris-flow initiated studies for real-time monitoring. Environ Geol 47(5):715–724
Catani F, Casagli N, Ermini L, Righini G, Menduni G (2005) Landslide hazard and risk mapping at catchment scale in the Arno River basin. Landslides 2(4):329–342
Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472
Convertino M, Troccoli A, Catani F (2013) Detecting fingerprints of landslide drivers: a MaxEnt model. J Geophys Res Earth Surf 118(3):1367–1386
Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K (2008) GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environ Geol 54(2):311–324
Dietrich WE, Reiss R, Hsu ML, Montgomery DR (1995) A process-based model for colluvial soil depth and shallow landsliding using digital elevation data. Hydrol Process 9(3–4):383–400
Dou J et al (2015) Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PLoS One 10(7):e0133262
Dudík M, Phillips SJ, Schapire RE (2007) Maximum entropy density estimation with generalized regularization and an application to species distribution modeling. J Mach Learn Res 8:1217–1260
Dyke J, Kleidon A (2010) The maximum entropy production principle: its theoretical foundations and applications to the earth system. Entropy 12(3):613–630
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(3):229–250
Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66(1):327–343
Fressard M, Thiery Y, Maquaire O (2014) Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the pays d'Auge plateau hillslopes (Normandy, France). Nat Hazards Earth Syst Sci 14(3):569–588
Glade T, Crozier M, Smith P (2000) Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical “antecedent daily rainfall model”. Pure Appl Geophys 157(6–8):1059–1079
Gökceoglu C, Aksoy H (1996) Landslide 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(1):147–161
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(1):181–216
Guzzetti F, Peruccacci S, Rossi M, Stark CP (2007) Rainfall thresholds for the initiation of landslides in central and southern Europe. Meteorog Atmos Phys 98(3–4):239–267
Hong Y, Hiura H, Shino K, Sassa K, Suemine A, Fukuoka H, Wang G (2005) The influence of intense rainfall on the activity of large-scale crystalline schist landslides in Shikoku Island, Japan. Landslides 2(2):97–105
Iida A (1984) Hydrologic method of estimation of topographic effect on saturated throughflow. Trans Jpn Geophys Union 5(1):1–12
Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36(7):1897–1910
Jakob M, Weatherly H (2003) A hydroclimatic threshold for landslide initiation on the north Shore Mountains of Vancouver, British Columbia. Geomorphology 54(3):137–156
Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11(3):425–439
Kawabata D, Bandibas J (2009) Landslide susceptibility mapping using geological data, a DEM from ASTER images and an artificial neural network (ANN). Geomorphology 113(1):97–109
Keefer DK et al (1987) Real-time landslide warning during heavy rainfall. Science 238(4829):921–926
Korean Geotechnical Society (2011) Research contract report: addition and complement causes survey of Mt. Woomyeon Landslide. Koran Geotechnical Society, Seoul, 268p
Korean Society of Civil Engineering (2012) Research contract report: causes survey and restoration work of Mt. Woomyeon Landslide. Korean Society of Civil Engineers, Seoul, 435p
Lanni C, McDonnell JJ, Rigon R (2011) On the relative role of upslope and downslope topography for describing water flow path and storage dynamics: a theoretical analysis. Hydrol Proc 25(25):3909–3923
Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Syst Sci 115(6):661–672
Lee S, Ryu JH, Won JS, Park HJ (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71(3):289–302
Martelloni G, Segoni S, Fanti R, Catani F (2012) Rainfall thresholds for the forecasting of landslide occurrence at regional scale. Landslides 9(4):485–495
Melillo M, Brunetti MT, Peruccacci S, Gariano SL, Guzzetti F (2015) An algorithm for the objective reconstruction of rainfall events responsible for landslides. Landslides 12(2):311–320
Melillo M, Brunetti MT, Peruccacci S, Gariano SL, Guzzetti F (2016) Rainfall thresholds for the possible landslide occurrence in Sicily (Southern Italy) based on the automatic reconstruction of rainfall events. Landslides 13(1):165–172
Menard S (1995) Applied logistic regression analysis. Sage, Thousand Oaks
Montgomery DR, Dietrich WE (1994) A physically based model for the topographic control on shallow landsliding. Water Resour Res 30(4):1153–1171
Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30
National Forestry Cooperative Federation (2011) Official archive for restoration work of Mt. Woomyeon landslide
Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3):171–191
O’brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41(5):673–690
Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37(9):1264–1276
Pachauri AK, Gupta PV, Chander R (1998) Landslide zoning in a part of the Garhwal Himalayas. Environ Geol 36(3–4):325–334
Park DW, Nikhil NV, Lee SR (2013) Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event. Nat Hazards Earth Syst Sci 13(11):2833–2849
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(1):95–118
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190(3):231–259
Pradhan AMS, Kim YT (2014) Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea. Nat Hazards 72(2):1189–1217
Pradhan AMS, Kim YT (2016a) Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping. Catena 140:125–139
Pradhan AMS, Kim YT (2016b) Spatial data analysis and application of evidential belief functions to shallow landslide susceptibility mapping at Mt. Umyeon, Seoul, Korea. Bull Eng Geol Environ 1–17. Published online first
Pradhan AMS, Kim YT (2017) GIS-based landslide susceptibility model considering effective contributing area for drainage time. Geocarto Int. doi:10.1080/10106049.2017.1303089 online first
Pradhan AMS, Kang HS, Lee S, Kim YT (2016) Spatial model integration for shallow landslide susceptibility and its runout using a GIS-based approach in Yongin, Korea. Geocarto Int 1–22
Rosi A, Segoni S, Catani F, Casagli N (2012) Statistical and environmental analyses for the definition of a regional rainfall threshold system for landslide triggering in Tuscany (Italy). J Geogr Sci 22(4):617–629
Safaei M, Omar H, Yousof ZB, Ghiasi V (2010) Applying geospatial technology to landslide susceptibility assessment. Electron J Geotech Eng 15:677–696
Safeland (2012) Statistical and empirical models for prediction of precipitation-induced landslides, Deliverable D1.5
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
Shahabi H, Hashim M, Ahmad BB (2015) Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran. Environ Earth Sci 73(12):8647–8668
Sidle RC, Pearce AJ, O'Loughlin CL (1985) Hillslope stability and land use. American geophysical union, Washington, D.C.
Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293
Tsangaratos P, Ilia I (2016) Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi perfection, Greece. Landslides 13(2):305–320
Turkington T, Ettema J, Van Westen CJ, Breinl K (2014) Empirical atmospheric thresholds for debris flows and flash floods in the southern French alps. Nat Hazards Earth Syst Sci 14(6):1517–1530
Van Westen CJ (2004) Geo-information tools for landslide risk assessment: an overview of recent developments. In: Landslides: evaluation and stabilization. CRC Press, Boca Raton, p 39–56
Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30(3):399–419
Venables WN, Ripley BD (2002) Statistics and computing. Springer, New York
Wu CH, Chen SC, Chou HT (2011) Geomorphologic characteristics of catastrophic landslides during typhoon Morakot in the Kaoping watershed, Taiwan. Eng Geol 123(1):13–21
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):1–12
Yune CY, Chae YK, Paik J, Kim G, Lee SW, Seo HS (2013) Debris flow in metropolitan area—2011 Seoul debris flow. J Mount Sci 10(2):199–206
Ziemer RR (1981) The role of vegetation in the stability of forested slopes. In: Proceedings of the international union of forest research organisations. Kyoto, Japan, p 297–308
Acknowledgements
This research was supported by the Public Welfare and Safety Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT, and Future Planning (grant No. 2012M3A2A1050977), a grant (13SCIPS04) from Smart Civil Infrastructure Research Program funded by Ministry of Land, Infrastructure and Transport (MOLIT) of Korea government and Korea Agency for Infrastructure Technology Advancement (KAIA) and the Brain Korea 21 Plus (BK 21 Plus).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Pradhan, A.M.S., Kang, HS., Lee, JS. et al. An ensemble landslide hazard model incorporating rainfall threshold for Mt. Umyeon, South Korea. Bull Eng Geol Environ 78, 131–146 (2019). https://doi.org/10.1007/s10064-017-1055-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10064-017-1055-y