Abstract
The main topic of this research was to evaluate the effect in the performance of stochastic landslide susceptibility models, produced by differences between the triggering events of the calibration and validation datasets. In the Caldera Ilopango area (El Salvador), MARS (multivariate adaptive regression splines)-based susceptibility modeling was applied using a set of physical–environmental predictors and two remotely recognized landslide inventories: one dated at 2003 (1503 landslides), which was the result of a normal rainfall season, and one which was produced by the combined effect of the Ida hurricane and the 96E tropical depression in 2009 (2237 landslides). Both the event inventories included shallow debris—flow or slide landslides, which involved the weathered mantle of the pyroclastic rocks that largely outcrop in the study area. To this aim, different model building and validation strategies were applied (self-validation, forward and backward chrono-validations), and their performances evaluated both through cutoff-dependent and -independent metrics. All of the tested models produced largely acceptable AUC (area under curve) values, albeit a loss in the predictive performance from self-validation to chrono-validation was observed. Besides, in terms of positive/negative predictions, some critical differences arose: using the 2009 extreme landslide inventory for calibration resulted in higher sensitivity but lower specificity; conversely, using the 2003 normal trigger landslide calibration inventory led to higher specificity but lower sensitivity, with relevant increase in type-II errors. These results suggest the need for investigating the extent of such effects, taking multitrigger intensities inventories as a standard procedure for susceptibility assessment in areas where extreme events potentially occur.
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References
Agostini S, Corti G, Doglioni C, Carminati E, Innocenti F, Tonarini S, Manetti P, Di Vincenzo G, Montanari D (2006) Tectonic and magmatic evolution of the active volcanic front in El Salvador: insight into the Berlín and Ahuachapán geothermal areas. Geothermics 35:368–408. https://doi.org/10.1016/j.geothermics.2006.05.003
Avila LA, Cangialosi J (2010) Tropical Cyclone Report Hurricane Ida. National Hurricane Center. https://www.nhc.noaa.gov/data/tcr/AL112009_Ida.pdf
Beven KJ, Kirkby MJ (1979) A physically based variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69. https://doi.org/10.1080/02626667909491834
Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13:361–378. https://doi.org/10.1007/s10346-015-0557-6
Burrough PA, McDonell RA (1998) Principles of geographical information systems. Oxford University Press, New York
Cama M, Lombardo L, Conoscenti C, Agnesi V, Rotigliano E (2015) Predicting storm-triggered debris flow events: Application to the 2009 Ionian Peloritan disaster (Sicily, Italy). Nat Hazard Earth Syst Sci 15(8):1785–1806. https://doi.org/10.5194/nhess-15-1785-2015
Cama M, Conoscenti C, Lombardo L, Rotigliano E (2016) Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy). Environ Earth Sci 75(238):1–21. https://doi.org/10.1007/s12665-015-5047-6
Cama M, Lombardo L, Conoscenti C, Rotigliano E (2017) Improving transferability strategies for debris flow susceptibility assessment. Application to the Saponara and Itala catchments (Messina, Italy). Geomorphology 288:52–65. https://doi.org/10.1016/j.geomorph.2017.03.025
CATHALAC Centro del Agua del Trópico Húmedo para América Latina y el Caribe (2009) Mapa de rutas de sistemas meteorológicos IDA y Baja 96E noviembre 2009. http://www.servir.net/images/desastres/2009-11-02_TT_Ida/ida_baja96_20091111.jpg
CEPAL (Comisión Económica para América Latina y el Caribe (2010) El Salvador: Impacto Socioeconómico, Ambiental y de Riesgo por la Baja Presión Asociada a la Tormenta Tropical Ida en Noviembre de 2009. Ciudad de México, pp. 21. http://repositorio.cepal.org/handle/11362/1382
CEPAL (Comisión Económica para América Latina y el Caribe (2011) El Salvador: Evaluación de daños y pérdidas en El Salvador ocasionados por la depresión tropical 12E. http://www.transparencia.gob.sv/institutions/mag/documents/119851/download
Chung CJ, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472. https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b
Conoscenti C, Ciaccio M, Caraballo-Arias NA, Gómez-Gutiérrez Á, Rotigliano E, Agnesi V (2015) Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: a case of the Belice River basin (western Sicily, Italy). Geomorphology 242:49–64. https://doi.org/10.1016/j.geomorph.2014.09.020
Conoscenti C, Rotigliano E, Cama M, Caraballo-Arias NA, Lombardo L, Agnesi V (2016) Exploring the effect of absence selection on landslide susceptibility models: a case study in Sicily. Italy Geomorphol 261:222–235. https://doi.org/10.1016/j.geomorph.2016.03.006
Conoscenti C, Agnesi V, Cama M, Caraballo-Arias NA, Rotigliano E (2018) Assessment of gully erosion susceptibility using multivariate adaptive regression splines and accounting for terrain connectivity. Land Degrad Dev 29:724–736. https://doi.org/10.1002/ldr.2772
Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Böhner J (2015) System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev 8:1991–2007. https://doi.org/10.5194/gmd-8-1991-2015
Costanzo D, Cappadonia C, Conoscenti C, Rotigliano E (2012a) Exporting a Google Earth™ aided earth-flow susceptibility model: a test in central Sicily. Nat Hazards 61(1):103–114. https://doi.org/10.1007/s11069-011-9870-0
Costanzo D, Rotigliano E, Irigaray C, Jiménez-Perálvarez JD, Chacón J (2012b) Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: Application to the river Beiro basin (Spain). Nat Hazards Earth Syst Sci 2(2):327–340. https://doi.org/10.5194/nhess-12-327-2012
Costanzo D, Chacón J, Conoscenti C, Irigaray C, Rotigliano E (2014) Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy). Landslides 11(4):639–653. https://doi.org/10.1007/s10346-013-0415-3
Craven P, Wahba G (1979) Smoothing noisy data with spline functions. Numer Math 31:377–403. https://doi.org/10.1007/BF01404567
Frattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111(1–4):62–72. https://doi.org/10.1016/j.enggeo.2009.12.004
Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1–141. http://www.jstor.org/stable/2241837
García-Rodríguez MJ, Malpica JA (2010) Assessment of earthquake-triggered landslide susceptibility in El Salvador based on an Artificial Neural Network model. Nat Hazards Earth Syst Sci 10(6):1307–1315. https://doi.org/10.5194/nhess-10-1307-2010
García-Rodríguez MJ, Malpica JA, Benito B, Díaz M (2008) Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology 95(3):172–191. https://doi.org/10.1016/j.geomorph.2007.06.001
Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184. https://doi.org/10.1016/j.geomorph.2006.04.007
Heckmann T, Gegg K, Gegg A, Becht M (2014) Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows. Nat Hazards Earth Syst Sci 14:259–278. https://doi.org/10.5194/nhess-14-259-2014
Hosmer DW, Lemeshow S (2000) Applied logistic regression, Wiley Series in Probability and Statistics. Wiley, Hoboken. https://doi.org/10.1198/tech.2002.s650
Jebur MN, Pradhan B, Tehrany MS (2014) Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ 152:150–165. https://doi.org/10.1016/j.rse.2014.05.013
Kirschbaum D, Stanley T, Yatheendradas S (2016) Modeling landslide susceptibility over large regions with fuzzy overlay. Landslides 13:485–496. https://doi.org/10.1117/12.737835
Kopačková V, Šebesta J (2007) An approach for GIS-based statistical landslide susceptibility zonation: with a case study in the northern part of El Salvador”, Proc. SPIE 6749, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VII, 67492R (29 October 2007). https://doi.org/10.1117/12.737835
Lexa J, Sebesta J, Chávez JA, Hernández W, Pecskay Z (2011) Geology and volcanic evolution in the southern part of the San Salvador Metropolitan Area. J Geosci 56(1):106–140. https://doi.org/10.3190/jgeosci.088
Lombardo L, Cama M, Maerker M, Rotigliano E (2014) A test of transferability for landslides susceptibility models under extreme climatic events: application to the Messina 2009 disaster. Nat Hazards 74(3):1951–1989. https://doi.org/10.1007/s11069-014-1285-2
Lombardo L, Cama M, Conoscenti C, Märker M, Rotigliano E (2015) Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messina (Sicily, southern Italy). Nat Hazards 79(3):1621–1648. https://doi.org/10.1007/s11069-015-1915-3
Lombardo L, Bachofer F, Cama M, Märker M, Rotigliano E (2016) Exploiting Maximum Entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north-eastern Sicily, Italy). Earth Surf Proc Land 41:1776–1789. https://doi.org/10.1002/esp.3998
MARN (Ministerio de Medio Ambiente y Recursos Naturales de Salvador, 2004) Memoria técnica para el mapa de susceptibilidad de deslizamientos de tierra en El Salvador
MARN (Ministerio de Medio Ambiente y Recursos Naturales de Salvador, 2009) Análisis de las intensidades máximas de lluvia. Evento atmosférico de los días 7 y 8 de noviembre de 2009. Informe, pp. 23
MARN (Ministerio de Medio Ambiente y Recursos Naturales de Salvador, 2010) Síntesis de los informes de evaluación técnica de las lluvias del 7 y 8 de noviembre 2009 en El Salvador: Análisis del impacto físico natural y vulnerabilidad socio ambiental. http://www.oas.org/summit/sisca/Download.aspx?type=C&lang=es&id=728
MARN (Ministerio de Medio Ambiente y Recursos Naturales de Salvador, 2011) Depresión Tropical 12E/Sistema Depresionario sobre El Salvador y otros eventos extremos del pacífico. https://reliefweb.int/report/el-salvador/depresi%C3%B3n-tropical-12e-sistema-depresionario-sobre-el-salvador-y-otros-eventos
Milborrow S (2015) Notes on the earth package [WWW Document]. URL http://www.milbo.org/doc/earth-notes.pdf
Milborrow S, Hastie T, Tibshirani R (2011) Earth: Multivariate Adaptive Regression Spline Models. R Software Package
Mora S, Vahrson WG (1991) Determinación “A priori” de la amenaza de deslizamientos en grandes áreas utilizando indicadores morfodinámicos. In CODAZZI A (1992) Memoria del primer simposio sobre sensores remotos y sistemas de información geográfica para el estudio de riesgos naturales. Bogotá, Colombia, organizado por el Instituto Geográfico Agostín Codazzi, UNESCO, ITC Holanda, 259–273
Mora S, Vahrson WG (1994) Macrozonation methodology by landslide hazard determination. Bull Soc Eng Geol 31:49–58
Naimi B (2015) Uncertainty Analysis for Species Distribution Models. R Software Package. https://doi.org/10.1111/j.1365-2699.2011.02523.x
R Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Riley SJ, DeGloria SD, Elliot R (1999) A terrain ruggedness index that quantifies topographic heterogeneity. Int J Sci 5(1–4):23–27
Rose WI, Bommer JJ, López DL, Carr MJ, Major JJ (eds, 2004) Natural hazards in El Salvador: Boulder, Colorado, Geological Society of America Special Paper 375, p
Rotigliano E, Agnesi V, Cappadonia C, Conoscenti C (2011) The role of the diagnostic areas in the assessment of landslide susceptibility models: a test in the Sicilian chain. Nat Hazards 58(3):981–999. https://doi.org/10.1007/s11069-010-9708-1
Rotigliano E, Cappadonia C, Conoscenti C, Costanzo D, Agnesi V (2012) Slope units-based flow susceptibility model: Using validation tests to select controlling factors. Nat Hazards 61(1):143–153. https://doi.org/10.1007/s11069-011-9846-0
Stoiber RE, Carr MJ (1973) Quaternary volcanic and tectonic segmentation of Central America. Bull Volcanol 37:204–325. https://doi.org/10.1007/BF02597631
Wilson JP, Gallant GC (2000) Digital terrain analysis. In: Wilson JP, Gallant JC (eds) Terrain analysis: principles and applications. Wiley, New York, pp 1–27
Youden WJ (1950) Index for rating diagnostic tests. Cancer 3 (1):32–35. https://doi.org/10.1002/1097-0142(1950)3:1%3C32::AID-CNCR2820030106%3E3.0.CO;2-3
Zevenbergen LW, Thorne CR (1987) Quantitative analysis of land surface topography. Earth Surf Process Landf 12:47–56. https://doi.org/10.1002/esp.3290120107
Acknowledgements
The present research was supported by a project funded by the Ministry of the Foreign Affairs of the Italian Government and carried out by the University of Palermo (resp. Prof. G. Giunta). Miguel Angél Hernandéz worked in this research as a PhD student of the Department of Earth and Marine Sciences of the University of Palermo (tutor E. Rotigliano, co-tutor C. Conoscenti). All the authors equally contributed to the research. The manuscript was linguistically reviewed by Maria Simona Romana.
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Rotigliano, E., Martinello, C., Hernandéz, M.A. et al. Predicting the landslides triggered by the 2009 96E/Ida tropical storms in the Ilopango caldera area (El Salvador, CA): optimizing MARS-based model building and validation strategies. Environ Earth Sci 78, 210 (2019). https://doi.org/10.1007/s12665-019-8214-3
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DOI: https://doi.org/10.1007/s12665-019-8214-3