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Space–Time Landslide Susceptibility Modeling Based on Data-Driven Methods

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Abstract

Delineating spatiotemporal variations in landslide susceptibility patterns is crucial for landslide prevention and management. In this study, we present a space–time modeling approach to predict the annual landslide susceptibility of the main island of Taiwan from 2004 to 2018. Specifically, we use a Bayesian version of the binomial generalized additive model, assuming that landslide occurrence follows a Bernoulli distribution. We generate 46,074 slope units to partition the island of Taiwan and divide the time domain into 14 annual units. The binary states of landslide presence and absence are classified by a set of static and dynamic covariates. Our modeling strategy features an initial explanatory model to test for goodness of fit and to interpret the effects of covariates. Then, five cross-validation schemes are tested to provide the full range of the predictive capacity of our model. We summarize the performance of each test through receiver operating characteristic curves and their numerical variation over space and time. Overall, our space–time model achieves satisfactory results, with the mean AUC above 0.8. We believe this type of dynamic prediction is a new direction that eventually moves away from the static view provided by traditional susceptibility models. Meanwhile, we believe that such analyses are only stepping stones for further improvements, the most natural of which are statistical simulations of future scenarios.

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Data and code availability statement

The data and codes that support this study can be accessed at: https://doi.org/10.5281/zenodo.7005143.

References

  • Alvioli M, Marchesini I, Reichenbach P, Rossi M, Ardizzone F, Fiorucci F, Guzzetti F (2016) Automatic delineation of geomorphological slope units with r. slopeunits v1. 0 and their optimization for landslide susceptibility modeling. Geosci Model Dev 9:3975

    Article  Google Scholar 

  • Atkinson PM, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24:373–385

    Article  Google Scholar 

  • Bakka H, Vanhatalo J, Illian JB, Simpson D, Rue H (2019) Non-stationary Gaussian models with physical barriers. Spatial Stat 29:268–288

    Article  Google Scholar 

  • Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Proc Land 16:427–445

    Article  Google Scholar 

  • Chang C-T, Wang H-C, Huang C-Y (2018) Assessment of MODIS-derived indices (2001–2013) to drought across Taiwan’s forests. Int J Biometeorol 62:809–822

    Article  Google Scholar 

  • Chang K-T, Merghadi A, Yunus AP, Pham BT, Dou J (2019) Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques. Sci Rep 9:12296

    Article  Google Scholar 

  • Chen C-W, Tung Y-S, Liou J-J, Li H-C, Cheng C-T, Chen Y-M (2019a) Assessing landslide characteristics in a changing climate in northern Taiwan. CATENA 175:263–277

    Article  Google Scholar 

  • Chen T-HK, Prishchepov AV, Fensholt R, Sabel CE (2019b) Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017. Remote Sens Environ 225:317–327

    Article  Google Scholar 

  • Chen Y-C, Chang K-T, Lee H-Y, Chiang S-H (2015) Average landslide erosion rate at the watershed scale in southern Taiwan estimated from magnitude and frequency of rainfall. Geomorphology 228:756–764

    Article  Google Scholar 

  • Chen YC, Chang Kt, Chiu YJ, Lau SM, Lee HY (2013) Quantifying rainfall controls on catchment-scale landslide erosion in Taiwan. Earth Surf Proc Land 38:372–382

    Article  Google Scholar 

  • Chung C-JF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472

    Article  Google Scholar 

  • Corominas J, van Westen C, Frattini P, Cascini L, Malet J-P, Fotopoulou S, Catani F, Van Den Eeckhaut M, Mavrouli O, Agliardi F (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263

    Google Scholar 

  • Fan X, Yunus AP, Scaringi G, Catani F, Siva Subramanian S, Xu Q, Huang R (2021) Rapidly evolving controls of landslides after a strong earthquake and implications for hazard assessments. Geophys Res Lett 48:e2020GL090509

    Article  Google Scholar 

  • Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Eng Geol 102:99–111

    Article  Google Scholar 

  • Goetz JN, Guthrie RH, Brenning A (2011) Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology 129:376–386

    Article  Google Scholar 

  • Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228:113–129

    Article  Google Scholar 

  • Gorsevski PV, Gessler PE, Boll J, Elliot WJ, Foltz RB (2006) Spatially and temporally distributed modeling of landslide susceptibility. Geomorphology 80:178–198

    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 

  • 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 

  • Hao L, Rajaneesh A, Van Westen C, Sajinkumar K, Martha TR, Jaiswal P, McAdoo BG (2020) Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis. Earth Syst Sci Data 12:2899–2918

    Article  Google Scholar 

  • Hosmer D, Lemeshow S (2000) Applied logistic regression, 2nd edn. Wiley, New York

    Book  Google Scholar 

  • Lin C-W, Chang W-S, Liu S-H, Tsai T-T, Lee S-P, Tsang Y-C, Shieh C-L, Tseng C-M (2011) Landslides triggered by the 7 August 2009 Typhoon Morakot in southern Taiwan. Eng Geol 123:3–12

    Article  Google Scholar 

  • Lin E, Liu C, Chang C, Cheng I, Ko M (2013) Using the formosat-2 high spatial and temporal resolution multispectral image for analysis and interpretation landslide disasters in taiwan. J Photogramm Remote Sens 17:31–51

    Google Scholar 

  • Lin G-F, Chang M-J, Huang Y-C, Ho J-Y (2017a) Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression. Eng Geol 224:62–74

    Article  Google Scholar 

  • Lin SC, Ke MC, Lo CM (2017b) Evolution of landslide hotspots in Taiwan. Landslides 14:1491–1501

    Article  Google Scholar 

  • Liu C-C (2015) Preparing a landslide and shadow inventory map from high-spatial-resolution imagery facilitated by an expert system. J Appl Remote Sens 9:096080

    Article  Google Scholar 

  • Loche M, Scaringi G, Yunus AP, Catani F, Tanyaş H, Frodella W, Fan X, Lombardo L (2022) Surface temperature controls the pattern of post-earthquake landslide activity. Sci Rep 12:988

    Article  Google Scholar 

  • Lombardo L, Opitz T, Ardizzone F, Guzzetti F, Huser R (2020) Space–time landslide predictive modelling. Earth-Sci Rev 209:103318

    Article  Google Scholar 

  • Lombardo L, Tanyas H (2020) Chrono-validation of near-real-time landslide susceptibility models via plug-in statistical simulations. Eng Geol 278:105818

    Article  Google Scholar 

  • Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Avtar R, Abderrahmane B (2020) Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth-Sci Rev 2020:103225

    Article  Google Scholar 

  • Monsieurs E, Dewitte O, Demoulin A (2019) A susceptibility-based rainfall threshold approach for landslide occurrence. Nat Hazards Earth Syst Sci 19:775–789

    Article  Google Scholar 

  • Reichenbach P, Rossi M, Malamud B, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth-Sci Rev 180:60–91

    Article  Google Scholar 

  • Rue H, Martino S, Chopin N (2009) Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc: Ser B (stat Methodol) 71:319–392

    Article  Google Scholar 

  • Samia J, Temme A, Bregt A, Wallinga J, Guzzetti F, Ardizzone F, Rossi M (2017) Characterization and quantification of path dependency in landslide susceptibility. Geomorphology 292:16–24

    Article  Google Scholar 

  • Scheidl C, Heiser M, Kamper S, Thaler T, Klebinder K, Nagl F, Lechner V, Markart G, Rammer W, Seidl R (2020) The influence of climate change and canopy disturbances on landslide susceptibility in headwater catchments. Sci Total Environ 742:140588

    Article  Google Scholar 

  • Shu H, Hürlimann M, Molowny-Horas R, González M, Pinyol J, Abancó C, Ma J (2019) Relation between land cover and landslide susceptibility in Val d’Aran, Pyrenees (Spain): historical aspects, present situation and forward prediction. Sci Total Environ 693:133557

    Article  Google Scholar 

  • Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc: Ser B (stat Methodol) 64:583–639

    Article  Google Scholar 

  • Steger S, Brenning A, Bell R, Petschko H, Glade T (2016) Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps. Geomorphology 262:8–23

    Article  Google Scholar 

  • Steger S, Mair V, Kofler C, Pittore M, Zebisch M, Schneiderbauer S (2021) Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling—Benefits of exploring landslide data collection effects. Sci Total Environ 776:145935

    Article  Google Scholar 

  • Tanyaş H, Lombardo L (2019) Variation in landslide-affected area under the control of ground motion and topography. Eng Geol 260:105229

    Article  Google Scholar 

  • Van den Bout B, Lombardo L, Chiyang M, van Westen C, Jetten V (2021) Physically-based catchment-scale prediction of slope failure volume and geometry. Eng Geol 284:105942

    Article  Google Scholar 

  • Van Westen C, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30:399–419

    Article  Google Scholar 

  • Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131

    Article  Google Scholar 

  • Verstappen HT (1983) Applied geomorphology: geomorphological survey for environmental development. Elsevier, Amsterdam

    Google Scholar 

  • Wang H, Yuan Z, Cheng Q, Zhang S, Sadeghi B (2022a) Geochemical anomaly definition using stream sediments landscape modeling. Ore Geol Rev 142:104715

    Article  Google Scholar 

  • Wang N, Cheng W, Marconcini M, Bachofer F, Liu C, Xiong J, Lombardo L (2022b) Space-time susceptibility modeling of hydro-morphological processes at the Chinese national scale. Eng Geol 301:106586

    Article  Google Scholar 

  • Worden C, Wald D (2016) ShakeMap manual online: technical manual, user’s guide, and software guide. US Geol Surv 1:156

    Google Scholar 

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Acknowledgements

This work was supported by the Joint Funds of the National Natural Science Foundation of China (U21A2013), the National Natural Science Foundation of China (42311530065), and the Fundamental Research Funds for National Universities, China University of Geosciences (Wuhan). This article was also partially supported by King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia, Grant URF/1/4338-01-01. We also thank the scientists of Taiwan that made the input data freely available.

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Authors and Affiliations

  1. School of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China

    Zhice Fang & Yi Wang

  2. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, PO Box 217, AE 7500, Enschede, The Netherlands

    Zhice Fang, Cees van Westen & Luigi Lombardo

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  1. Zhice Fang
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  2. Yi Wang
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Correspondence to Yi Wang.

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Fang, Z., Wang, Y., van Westen, C. et al. Space–Time Landslide Susceptibility Modeling Based on Data-Driven Methods. Math Geosci (2023). https://doi.org/10.1007/s11004-023-10105-6

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