Skip to main content

Advertisement

SpringerLink
Log in

Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy)

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Debris flows are among the most hazardous phenomena in nature, requiring the preparation of susceptibility models in order to cope with this severe threat. The aim of this research was to verify whether a grid cell-based susceptibility model was capable of predicting the debris-flow initiation sites in the Giampilieri catchment (10 km2), which was hit by a storm on the 1st October 2009, resulting in more than one thousand landslides. This kind of event is to be considered as recurrent in the area as attested by historical data. Therefore, predictive models have been prepared by using forward stepwise binary logistic regression (BLR), a landslide inventory and a set of geo-environmental attributes as predictors. In particular, the effects produced in the quality of the predictive models by changing the grid cell size (2, 4, 16 and 32 m) have been explored in terms of predictive performance, robustness, importance and role of the selected predictors. The results generally attested for high predictive performances of the 2, 8 and 16 m model sets (AUROC > 0.8), with the latter producing slightly better predictions and the 32 m showing the worst yet still acceptable performance and the lowest robustness. As regards the predictors, although all the 4 sets of models share a common group (topographic attributes, outcropping lithology and land use), the similarity resulted higher between the 8 and 16 m sets. The research demonstrates that no meaningful loss in the predictive performance arises by adopting a coarser cell size for the mapping unit. However, the largest adopted cell size resulted in marginally worse model performance, with AUROC slightly below 0.8 and error rates above 0.3.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Agnesi V, Rasà R, Puglisi C et al (2009) La franosità diffusa dell’1 Ottobre 2009 nel territorio ionico-peloritano della Provincia di Messina: stato delle indagini e prime considerazioni sulle dinamiche geomorfiche attivate. Geologi di Sicilia 4:23–30

    Google Scholar 

  • Akgün A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9:93–106. doi:10.1007/s10346-011-0283-7

    Article  Google Scholar 

  • Ardizzone F, Basile G, Cardinali M et al (2012) Landslide inventory map for the Briga and the Giampilieri catchments, NE Sicily, Italy. J Maps 8:176–180. doi:10.1080/17445647.2012.694271

    Article  Google Scholar 

  • Aronica GT, Brigandí G, Morey N (2012) Flash floods and debris flow in the city area of Messina, north-east part of Sicily, Italy in October 2009: the case of the Giampilieri catchment. Nat Hazards Earth Syst Sci 12:1295–1309. doi:10.5194/nhess-12-1295-2012

    Article  Google Scholar 

  • Atkinson PM, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24(4):373–385. doi:10.1016/S0098-3004(97)00117-9

    Article  Google Scholar 

  • Bai SB, Wang J, Lü GN et al (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the three Gorges area, China. Geomorphology 115:23–31. doi:10.1016/j.geomorph.2009.09.025

    Article  Google Scholar 

  • Cama M, Lombardo L, Conoscenti C et al (2015) Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy). Nat Hazards Earth Syst Sci 15:1785–1806. doi:10.5194/nhess-15-1785-2015

    Article  Google Scholar 

  • Carrara A, Cardinali M, Detti R et al (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Process Landf 16:427–445. doi:10.1002/esp.3290160505

    Article  Google Scholar 

  • Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1995) GIS technology in mapping landslide hazard. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer Academic Publisher, Dordrecht, pp 135–175

    Chapter  Google Scholar 

  • Carrara A, Guzzetti F, Cardinali M, Reichenbach P (1999) Use of GIS technology in the prediction and monitoring of landslide hazard. Nat Hazards 20:117–135. doi:10.1023/A:1008097111310

    Article  Google Scholar 

  • Carrara A, Crosta G, Frattini P (2003) Geomorphological and historical data in assessing landslide hazard. Earth Surf Process Landforms 28:1125–1142. doi:10.1002/esp.545

    Article  Google Scholar 

  • Chung C-JF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472. doi:10.1023/B:NHAZ.0000007172.62651.2b

    Article  Google Scholar 

  • Claessens L, Heuvelink GBM, Schoorl JM, Veldkamp A (2005) DEM resolution effects on shallow landslide hazard and soil redistribution modelling. Earth Surf Process Landf 30:461–477. doi:10.1002/esp.1155

    Article  Google Scholar 

  • Coe JA, Godt JW, Baum RL et al (2004) Landslide susceptibility from topography in Guatemala. Landslides Eval Stab 1:69–78

    Google Scholar 

  • Conoscenti C, Di Maggio C, Rotigliano E (2008) GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy). Geomorphology 94(3):325–339. doi:10.1016/j.geomorph.2006.10.039

    Article  Google Scholar 

  • Conoscenti C, Angileri S, Cappadonia C et al (2014) Gully erosion susceptibility assessment by means of GIS-based logistic regression: a case of Sicily (Italy). Geomorphology 204:399–411. doi:10.1016/j.geomorph.2013.08.021

    Article  Google Scholar 

  • Conoscenti C, Ciaccio M, Caraballo-Arias NA et al (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. doi:10.1016/j.geomorph.2014.09.020

    Article  Google Scholar 

  • Costanzo D, Rotigliano E, Irigaray C et al (2012a) 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 12:327–340. doi:10.5194/nhess-12-327-2012

    Article  Google Scholar 

  • Costanzo D, Cappadonia C, Conoscenti C, Rotigliano E (2012b) Exporting a Google Earth™ aided earth-flow susceptibility model: a test in central Sicily. Nat Hazards 61:103–114. doi:10.1007/s11069-011-9870-0

    Article  Google Scholar 

  • Costanzo D, Chacón J, Conoscenti C et al (2014) Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy). Landslides 11:639–653. doi:10.1007/s10346-013-0415-3

    Article  Google Scholar 

  • Crozier MJ (2005) Multiple-occurrence regional landslide events in New Zealand: hazard management issues. Landslides 2:247–256. doi:10.1007/s10346-005-0019-7

    Article  Google Scholar 

  • Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228. doi:10.1016/S0169-555X(01)00087-3

    Article  Google Scholar 

  • Dai FC, Lee CF (2003) A spatiotemporal probabilistic modelling of storm-induced shallow landsliding using aerial photographs and logistic regression. Earth Surf Process Landf 28:527–545. doi:10.1002/esp.456

    Article  Google Scholar 

  • De Guidi G, Scudero S (2013) Landslide susceptibility assessment in the Peloritani Mts. (Sicily, Italy) and clues for tectonic control of relief processes. Nat Hazards Earth Syst Sci 13:949–963. doi:10.5194/nhess-13-949-2013

    Article  Google Scholar 

  • Del Ventisette C, Garfagnoli F, Ciampalini A et al (2012) An integrated approach to the study of catastrophic debris-flows: geological hazard and human influence. Nat Hazards Earth Syst Sci 12:2907–2922. doi:10.5194/nhess-12-2907-2012

    Article  Google Scholar 

  • 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 9:383–400. doi:10.1002/hyp.3360090311

    Article  Google Scholar 

  • Felicísimo ÁM, Cuartero A, Remondo J, Quirós E (2012) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10:175–189. doi:10.1007/s10346-012-0320-1

    Article  Google Scholar 

  • Fiannacca P, Williams IS, Cirrincione R, Pezzino A (2008) Crustal contributions to late hercynian peraluminous magmatism in the Southern Calabria-Peloritani Orogen, Southern Italy: petrogenetic inferences and the gondwana connection. J Petrol 49:1497–1514. doi:10.1093/petrology/egn035

    Article  Google Scholar 

  • Frattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111:62–72. doi:10.1016/j.enggeo.2009.12.004

    Article  Google Scholar 

  • Ghisetti F, Vezzani L (2002) Normal faulting, transcrustal permeability and seismogenesis in the Apennines (Italy). Tectonophysics 348:155–168. doi:10.1016/S0040-1951(01)00254-2

    Article  Google Scholar 

  • Gómez Gutiérrez A, Schnabel S, Lavado Contador F (2009) Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies. Ecol Model 220:3630–3637. doi:10.1016/j.ecolmodel.2009.06.020

    Article  Google Scholar 

  • Gómez Gutiérrez A, Conoscenti C, Angileri SE et al (2015) Using Topographical attributes to model the spatial distribution of gullying from two Mediterranean basins: advantages and limitations. Nat Hazards. doi:10.1007/s11069-015-1703-0

    Google Scholar 

  • Guns M, Vanacker V (2012) Logistic regression applied to natural hazards: rare event logistic regression with replications. Nat Hazards Earth Syst Sci 12:1937–1947. doi:10.5194/nhess-12-1937-2012

    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 

  • Guzzetti F, Reichenbach P, Cardinali M et al (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72:272–299. doi:10.1016/j.geomorph.2005.06.00

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Ardizzone F et al (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184. doi:10.1016/j.geomorph.2006.04.007

    Article  Google Scholar 

  • Guzzetti F, Mondini AC, Cardinali M et al (2012) Landslide inventory maps: new tools for an old problem. Earth-Sci Rev 112:42–66. doi:10.1016/j.earscirev.2012.02.001

    Article  Google Scholar 

  • Heckmann T, Gegg K, GeggBecht M 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. doi:10.5194/nhess-14-259-2014

    Article  Google Scholar 

  • Hosmer DW, Lemeshow S (2000) Applied logistic regression. Ser Probab Stat 2:375. doi:10.1198/tech.2002.s650

    Google Scholar 

  • Hungr O, Evans SG, Bovis MJ, Hutchinson JN (2001) A review of the classification of landslides of the flow type. Environ Eng Geosci 7:221–238. doi:10.2113/gseegeosci.7.3.221

    Article  Google Scholar 

  • Hungr O, McDougall S, Bovis M (2005) Entrainment of material by debris flows. Debris-flow Hazards and Related Phenomena. Springer, Berlin, pp 135–158

    Book  Google Scholar 

  • Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11:167–194. doi:10.1007/s10346-013-0436-y

    Article  Google Scholar 

  • Hutchinson JN (1988) General report: morphological and geotechnical parameters of landslides in relation to geology and hydrogeology. Landslides 26:3–35

    Google Scholar 

  • Igwe O, Mode W, Nnebedum O et al (2014) The analysis of rainfall-induced slope failures at Iva Valley area of Enugu State, Nigeria. Environ Earth Sci 71:2465–2480. doi:10.1007/s12665-014-4009-8

    Article  Google Scholar 

  • Kienzle S (2004) The effect of DEM raster resolution on first order, second order and compound terrain derivatives. Trans GIS 8:83–111. doi:10.1111/j.1467-9671.2004.00169.x

    Article  Google Scholar 

  • Köppen W (1923) Die klimate der Erde. Walter de Gruyter, Berlin

    Google Scholar 

  • Lee S, Choi J, Woo I (2004) The effect of spatial resolution on the accuracy of landslide susceptibility mapping: a case study in Boun, Korea. Geosci J 8:51–60. doi:10.1007/BF02910278

    Article  Google Scholar 

  • Legorreta Paulin G, Bursik M, Lugo-Hubp J, Zamorano Orozco JJ (2010) Effect of pixel size on cartographic representation of shallow and deep-seated landslide, and its collateral effects on the forecasting of landslides by SINMAP and Multiple Logistic Regression landslide models. Phys Chem Earth 35:137–148. doi:10.1016/j.pce.2010.04.008

    Article  Google Scholar 

  • Lentini F, Catalano S, Carbone S (2000) Note illustrative della Carta Geologica della Provincia di Messina, scala 1: 50.000. Provincia Regionale di Messina, Assessorato Servizio Territorio—Servizio Geologico d’Italia

  • Lentini F, Carbone S, Messina A et al (2007) Carta Geologica d’Italia scala 1:50.000 Foglio 601 “Messina-Reggio di Calabria”, con note illustrative. APAT (Agenzia per la protezione dell’ambiente e per i servizi tecnici), Dipartimento Difesa del Suolo—Servizio geologico d’Italia

  • 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 74:1951–1989. doi:10.1007/s11069-014-1285-2

    Article  Google Scholar 

  • Lombardo L, Cama M, Conoscenti C et al (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. doi:10.1007/s11069-015-1915-3

    Google Scholar 

  • Mathew J, Jha VK, Rawat GS (2008) Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides 6:17–26. doi:10.1007/s10346-008-0138-z

    Article  Google Scholar 

  • McFadden D (1978) Quantitative methods for analyzing travel behaviour of individuals: some recent developments. In: Hensher DA, Stopher PR (eds) Behav Travel Model. Croom Helm, London, pp 279–318

  • Melani S, Pasi F, Gozzini B, Ortolani A (2013) A four year (2007-2010) analysis of long-lasting deep convective systems in the Mediterranean basin. Atmos Res 123:151–166. doi:10.1016/j.atmosres.2012.09.006

    Article  Google Scholar 

  • Messina A, Somma R, Careri G et al (2004) Peloritani continental crust composition (southern Italy): geological and petrochemical evidences. Boll Soc Geol Ital 123:405–444

    Google Scholar 

  • Minder JR, Roe GH, Montgomery DR (2009) Spatial patterns of rainfall and shallow landslide susceptibility. Water Resour Res 45:1–11. doi:10.1029/2008WR007027

    Google Scholar 

  • Mondini AC, Guzzetti F, Reichenbach P et al. (2011) Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images. Remote Sens Environ 115:1743–1757. doi:10.1016/j.rse.2011.03.006

    Article  Google Scholar 

  • Montgomery DR, Dietrich WE (1994) A physically based model for the topographic control on shallow landsliding. Water Resour Res 30:1153–1171. doi:10.1029/93WR02979

    Article  Google Scholar 

  • Nagelkerke NJD (1991) A note on a general definition of the coefficient of determination. Biometrika 78:691–692. doi:10.1093/biomet/78.3.691

    Article  Google Scholar 

  • 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 97:171–191. doi:10.1016/j.enggeo.2008.01.004

    Article  Google Scholar 

  • Ohlmacher GC (2007) Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Eng Geol 91:117–134. doi:10.1016/j.enggeo.2007.01.005

    Article  Google Scholar 

  • Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69:331–343. doi:10.1016/S0013-7952(03)00069-3

    Article  Google Scholar 

  • Palamakumbure D, Flentje P, Stirling D (2015) Consideration of optimal pixel resolution in deriving landslide susceptibility zoning within the Sydney Basin, New South Wales, Australia. Comput Geosci 82:13–22. doi:10.1016/j.cageo.2015.05.002

    Article  Google Scholar 

  • Penna D, Borga M, Aronica GT et al (2014) The influence of grid resolution on the prediction of natural and road-related shallow landslides. Hydrol Earth Syst Sci 18:2127–2139. doi:10.5194/hess-18-2127-2014

    Article  Google Scholar 

  • Petschko H, Brenning A, Bell R et al (2014) Assessing the quality of landslide susceptibility maps—case study lower Austria. Nat Hazards Earth Syst Sci 14:95–118. doi:10.5194/nhess-14-95-2014

    Article  Google Scholar 

  • Protezione Civile Nazionale (2009) Landslide and mud flood emergency Messina Province, Italy. Application for assistance from the European Union Solidarity Fund (EUSF), Council Regulation (EC), 2012/2002, Article 4

  • Rakotomalala R (2005) Tanagra: un logiciel gratuit pour l’enseignement et la recherche. Actes De EGC 697–702

  • Rossi M, Guzzetti F, Reichenbach P et al (2010) Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology 114:129–142. doi:10.1016/j.geomorph.2009.06.020

    Article  Google Scholar 

  • 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 58:981–999. doi:10.1007/s11069-010-9708-1

    Article  Google Scholar 

  • Rotigliano E, Cappadonia C, Conoscenti C et al (2012) Slope units-based flow susceptibility model: using validation tests to select controlling factors. Nat Hazards 61:143–153. doi:10.1007/s11069-011-9846-0

    Article  Google Scholar 

  • Süzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45:665–679. doi:10.1007/s00254-003-0917-8

    Article  Google Scholar 

  • Tagil S, Jenness J (2008) GIS-based automated landform classification and topographic, landcover and geologic attributes of landforms around the Yazoren Polje, Turkey. J Appl Sci 8:910–921. doi:10.3923/jas.2008.910.921

    Article  Google Scholar 

  • Tarolli P, Tarboton DG (2006) A new method for determination of most likely landslide initiation points and the evaluation of digital terrain model scale in terrain stability mapping. Hydrol Earth Syst Sci 10:663–677. doi:10.5194/hess-10-663-2006

    Article  Google Scholar 

  • Tian Y, Xiao C, Liu Y, Wu L (2008) Effects of raster resolution on landslide susceptibility mapping: a case study of Shenzhen. Sci China Ser E: Technol Sci 51:188–198. doi:10.1007/s11431-008-6009-y

    Article  Google Scholar 

  • Van Den Eeckhaut M, Reichenbach P, Guzzetti F et al (2009) Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium. Nat Hazards Earth Syst Sci 9:507–521. doi:10.5194/nhess-9-507-2009

    Article  Google Scholar 

  • Varnes DJ (1978) Slope movement: types and process. In: Schuster RL, Krizek RJ (eds) Landslides: analyses and control, transportation research board, vol 176. Natural Academy of Science, Washington, pp 11–33

    Google Scholar 

  • Vilimek V, Klimes J, Emmer A et al (2015) Geomorphologic impacts of the glacial lake outburst flood from Lake No. 513 (Peru). Environ Earth Sci 73:5233–5244. doi:10.1007/s12665-014-3768-6

    Article  Google Scholar 

  • Vorpahl P, Elsenbeer H, Märker M, Schröder B (2012) How can statistical models help to determine driving factors of landslides? Ecol Modell 239:27–39. doi:10.1016/j.ecolmodel.2011.12.007

    Article  Google Scholar 

  • Weiss A (2001) Topographic position and landforms analysis. Poster Present ESRI User Conf San Diego, CA 64:227–245

    Google Scholar 

  • Wilson JP, Gallant JC (2000) Secondary topographic attributes. Terrain Anal Princ Appl 87–132

  • Wilson JP, Reppeto PL, Snyder D (2000) Effect of data source, grid resolution, and flow-routing method on computed topographic attributes. Terrain Anal Princ Appl 133–161

  • Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85:274–287. doi:10.1016/j.catena.2011.01.014

    Article  Google Scholar 

Download references

Acknowledgments

The findings and discussion of this research are the results of the activity which was carried out in the framework of the Ph.D. research projects of Mariaelena Cama and Luigi Lombardo at the “Dipartimento di Scienze della Terra e del Mare” of the University of Palermo (XXV cycle), supervisor Prof. E. Rotigliano. Luigi Lombardo Ph.D. thesis was internationally co-tutored with the Department of Geography of the University of Tübingen (Deutschland). This research was supported by the project SUFRA_SICILIA, funded by the ARTA-Regione Sicilia, and the FFR 2012/2013 Project, funded by the University of Palermo. Authors have commonly shared all the part of the research as well as of the manuscript preparation. Clare Hampton has linguistically edited the final version of this text. Authors wish to thank two anonymous reviewers which allowed to improve the quality of the manuscript.

Author information

Authors and Affiliations

  1. Dipartimento di Scienze della Terra e del Mare, Università degli Studi di Palermo, Palermo, Italy

    M. Cama, C. Conoscenti, L. Lombardo & E. Rotigliano

  2. Department of Physical Geography and GIS, University of Tübingen, Tübingen, Germany

    L. Lombardo

Authors
  1. M. Cama
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Conoscenti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L. Lombardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. E. Rotigliano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Cama.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cama, M., Conoscenti, C., Lombardo, L. et al. 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 (2016). https://doi.org/10.1007/s12665-015-5047-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-015-5047-6

Keywords

Search

Navigation