Abstract
Assessment of the spatial probability of future landslide occurrences for disaster risk reduction is done through landslide susceptibility modelling. In this study, we investigated the effect of time and space partitioning strategies of samples on the performance of regional landslide susceptibility models on macro-scale mapping in the state of Mizoram, India, covering 21,087 km2 area. We used landslide inventory data of 2014 and 2017 periods consisting of 1205 and 2265 landslides, respectively, to train and test the models with four sampling strategies such as spatial, temporal, temporal (size constrained) and temporal (geographic constrained). We used five commonly inherited models such as multiclass weighted overlay (MCWO), information value (IV), weights of evidence (WoE), logistic regression (LR) and artificial neural network (ANN) to evaluate the effect of sampling strategies on the model performance for regional landslide susceptibility mapping. Validation of model performance was done using receiver operating characteristic (ROC) curve. Traditional spatial sampling strategy applied to landslides in 2014 with a random split in 70:30 proportion provided a high performance of all the five models but failed to predict landslides in 2017. The landslide incidences in 2017, when used for model validation either entirely or in different split conditions (both size and geographic constrained), provided consistent performance, even though the testing sample size is large or have a different spatial disposition, if the training was carried out with non-linear susceptibility models such as LR and ANN using landslide incidences in 2014. Results show the importance of sample selection during validation of landslide susceptibility models on a regional scale.
Change history
02 March 2021
A Correction to this paper has been published: https://doi.org/10.1007/s10346-021-01646-0
References
Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44
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(1-2):15–31
Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design and application. J Microbiol Methods 43:3–31
Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
Blahut J, Van Westen CJ, Sterlacchini S (2010) Analysis of landslide inventories for accurate prediction of debris-flow source areas. Geomorphology 119(1-2):36–51. https://doi.org/10.1016/j.geomorph.2010.02.017
Bonham-Carter GF (1994) Geographic information systems for geoscientists: modelling with GIS. Computer Methods in Geosciences, vol. 13. Pergamon Press, Oxford, p 398
Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Process Landf 16:427–445
Cevasco A, Pepe G, Brandolini P (2014) The influences of geological and land-use settings on shallow landslides triggered by an intense rainfall event in a coastal terraced environment. Bull Eng Geol Environ 73(3):859–875
Chung CJ, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472
Di Napoli M, Carotenuto F, Cevasco A, Confuorto P, Di Martire D, Firpo M, Pepe G, Raso E, Calcaterra D (2020) Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability. Landslides 17(8):1897–1914
EM-DAT (2019) www.emdat.be
Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66:327–343
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874
Frattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111:62–72
Froude MJ, Petley D (2018) Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci 18:2161–2181
Ghosh S, Carranza EJM, Van Westen CJ, Jetten VG, Bhattacharya DN (2011) Selecting and weighting spatial predictors for empirical modelling of landslide susceptibility in the Darjeeling Himalayas (India). Geomorphology 131:35–56
GSI (2020) www.bhukosh.gsi.gov.in, Geology 50K map of Mizoram, Accessed on 13 February 2020
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(1-4):181–216
Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72:272–299
Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1-2):166–184
Gόmez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78:11–27
Hutchinson JN (1995) Keynote paper: landslide hazard assessment. In: Bell (ed) Landslides. Balkema, Rotterdam, pp 1805–1841
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(3-4):347–366
Kanungo D, Arora M, Sarkar S, Gupta R (2009) Landslide susceptibility zonation (LSZ) mapping - a review. J S Asia Disast Stud 2(1):81–105
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
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(7):1477–1491
Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol 50:847–855
Lee S, Choi J, Min K (2002) Landslide susceptibility analysis and verification using the Bayesian probability model. Environ Geol 43(12):120–131
Lee S, Choi J, Min K (2004) Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. Int J Remote Sens 25(11):2037–2052. https://doi.org/10.1080/01431160310001618734
Lee S, Ryu J-H, Won J-S, Park H-J (2004) Determination and application of the weights for landslide susceptibility mapping using artificial neural network. Eng Geol 71:289–302
Lombardo L, Opitz T, Ardizzone F, Guzzetti F, Huser R (2020) Space-time landslide predictive modelling. Earth Sci Rev 209:103318
Martha TR, Kerle N, Jetten V, van Westen CJ, Kumar KV (2010) Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology 116(1-2):24–36
Martha TR, Kerle N, van Westen CJ, Jetten V, Kumar KV (2011) Segment optimisation and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Trans Geosci Remote Sens 49(12):4928–4943
Martha TR, Kamala P, Jose J, Kumar KV, Sankar GJ (2016) Identification of new landslides from high resolution satellite data covering a large area using object-based change detection methods. J Indian Soc Remote Sens 44(4):515–524
Mathew J, Jha VK, Rawat GS (2007) Weights of evidence modelling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand. Curr Sci 92(5):628–638
Mathew J, Babu DG, Kundu S, Vinod Kumar K, Pant CC (2014) Integrating intensity–duration-based rainfall threshold and antecedent rainfall-based probability estimate towards generating early warning for rainfall-induced landslides in parts of the Garhwal Himalaya, India. Landslides 11(4):575–588
Montrasio L, Valentino R, Corina A, Rossi L, Rudari R (2014) A prototype system for space-time assessment of rainfall-induced shallow landslides in Italy. Nat Hazards 74(2):1263–1290
Neuhäuser B, Terhorst B (2007) Landslide susceptibility assessment using “weightsof-evidence” applied to a study area at the Jurassic escarpment (SW-Germany). Geomorphology 86:12–24
NRSC (2012) NRSC Technical Document: manual for geomorphologyand lineament mapping. Document reference number: NRSC-RSAA-ERG-G&GD-SEP' 12-TR-445
NRSC (2014) Land use/land cover database on 1:50,000 scale, Natural Resources Census Project, LUCMD, LRUMG, RSAA. Hyderabad, National Remote Sensing Centre, ISRO
Paola JD, Schowengerdt RA (1995) A review and analysis of back propagation neural networks for classification of remotely sensed multi-spectral imagery. Int J Remote Sens 16:3033–3058
Pardeshi SD, Autade SE, Pardeshi SS (2013) Landslide hazard assessment: recent trends and techniques. SpringerPlus 2(1):523. https://doi.org/10.1186/2193-1801-2-523
Pellicani R, Argentiero I, Spilotro G (2017) GIS-based predictive models for regional-scale landslide susceptibility assessment and risk mapping along road corridors. Geomat Nat Haz Risk 8(2):1012–1033
Pradhan B, Youssef A, Varathrajo R (2010) Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model. Geo-spatial Inf Sci 13(2):93–102. https://doi.org/10.1007/s11806-010-0236-7
Pudi R, Roy P, Martha TR, Kumar KV, Rao PR (2018) Spatial potential analysis of earthquakes in the western Himalayas using b-value and thrust association. J Geol Soc India 91(6):664–670
Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91
Remondo J, González A, De Terán JRD, Cendrero A, Fabbri A, Chung C-JF (2003) Validation of landslide susceptibility maps; examples and applications from a case study in Northern Spain. Nat Hazards 30:437–449. https://doi.org/10.1023/B:NHAZ.0000007201.80743.fc
Sarkar S, Roy AK, Martha TR (2013) Landslide susceptibility assessment using information value method in parts of the Darjeeling Himalayas. J Geol Soc India 82(4):351–362
Sawatzky DL, Raines GL, Bonham-Carter GF, Looney CG (2009) Spatial data modeller (SDM): ArcMAP 9.3 geoprocessing tools for spatial data modelling using weights of evidence, logistic Regression, fuzzy logic and neural networks. http://arcscripts.esri.com/details.asp?dbid=15341
Segoni S, Pappafico G, Luti T, Catani F (2020) Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterisation. Landslides. 17:2443–2453. https://doi.org/10.1007/s10346-019-01340-2
Sepe C, Confuorto P, Angrisani AC, Di Martire D, Di Napoli M, Calcaterra D (2019) Application of a statistical approach to landslide susceptibility map generation in urban settings. In: Shakoor A, Cato K (eds) IAEG/AEG Annual meeting proceedings, San Francisco, California, 2018 - Volume 1. Springer, Cham, pp 155–162. https://doi.org/10.1007/978-3-319-93124-1_19
SPSS (2017) SPSS for Windows, Version 23, 2017. SPSS Inc., Chicago
Taalab K, Cheng T, Zhang Y (2018) Mapping landslide susceptibility and types using random forest. Big Earth Data 2(2):159–178
Vahidnia NH, Alesheikh AA, Mohommad A, Hosseinalli F (2010) A GIS based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci 36(9):1101–1114
Vakhshoori V, Pourghasemi HR, Zare M, Blaschke T (2019) Landslide susceptibility mapping using GIS-based data mining algorithms. Water 11(11):2292
Valdiya KS (2016) The making of India: geodynamic evolution. Society of Earth Scientists Series. Springer, Cham 924p
Van Westen CJ (1993) Application of geographic information systems landslide hazard zonation. ITC Publication 15
Van Westen CJ, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation. Geol Rundsch 86(2):404–414
Varnes DJ (1984) IAEG Commission on landslides and other mass-movements landslide hazard zonation: a review of principles and practice. UNESCO Press, Paris, 63 pp
Xiao T, Segoni S, Chen L, Yin K. Casagli N (2020) A step beyond landslide susceptibility maps: a simple method to investigate and explain the different outcomes obtained by different approaches. Landslides 17(3): 627-640.
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
Yin KL, Yan TZ (1988) Statistical prediction models for slope instability of metamorphosed rocks. In: Bonnard C (ed) Proc 5th INternational Symposium on landslides, Pub Rotterdam: A Blakema, Lausanne, pp 1269–1272
Acknowledgements
We thank Shri Santanu Chowdhury, Director, National Remote Sensing Centre (NRSC), and Dr. P. V. N. Rao, Deputy Director (Remote Sensing Applications Area), NRSC, for their support and encouragement. Critical comments of two anonymous reviewers have helped us to improve the study, and we are grateful to them. We are thankful to the Geological Survey of India (GSI) and Mizoram Remote Sensing Application Centre (MIRSAC) for providing the Geological and Soil map, respectively, of the state.
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The original online version of this article was revised: This article has an error that was introduced during the publishing process. In this paper, Eq. 3 is mistakenly presented and Table 4 was incorrectly laid out. The correct Eq. 3and Table 4 are provided here.
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Khanna, K., Martha, T.R., Roy, P. et al. Effect of time and space partitioning strategies of samples on regional landslide susceptibility modelling. Landslides 18, 2281–2294 (2021). https://doi.org/10.1007/s10346-021-01627-3
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DOI: https://doi.org/10.1007/s10346-021-01627-3