A ROC analysis-based classification method for landslide susceptibility maps

[EN] A landslide susceptibility map is a crucial tool for landuse spatial planning and management in mountainous areas. An essential issue in such maps is the determination of susceptibility thresholds. To this end, the map is zoned into a limited number of classes. Adopting one classification system or another will not only affect the map's readability and final appearance, but most importantly, it may affect the decision-making tasks required for effective land management. The present study compares and evaluates the reliability of some of the most commonly used classification methods, applied to a susceptibility map produced for the area of La Marina (Alicante, Spain). A new classification method based on ROC analysis is proposed, which extracts all the useful information from the initial dataset (terrain characteristics and landslide inventory) and includes, for the first time, the concept of misclassification costs. This process yields a more objective differentiation of susceptibility levels that relies less on the intrinsic structure of the terrain characteristics. The results reveal a considerable difference between the classification methods used to define the most susceptible zones (in over 20% of the surface) and highlight the need to establish a standard method for producing classified susceptibility maps. The method proposed in the study is particularly notable for its consistency, stability and homogeneity, and may mark the starting point for consensus on a generalisable classification method.Cantarino-Martí, I.; Carrión Carmona, MÁ.; Goerlich-Gisbert, F.; Martínez Ibáñez, V. (2018). A ROC analysis-based classification method for landslide susceptibility maps. Landslides. 1-18. doi:10.1007/s10346-018-1063-4118Armstrong MP, Xiao N, Bennett DA (2003) Using genetic algorithms to create multicriteria class intervals for choropleth maps. Ann Assoc Am Geogr 93(3):595–623. https://doi.org/10.1111/1467-8306.9303005Atkinson P, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24(4):373–385. https://doi.org/10.1016/S0098-3004(97)00117-9Ayalew 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. https://doi.org/10.1016/j.geomorph.2004.06.010Baeza C, Lantada N, Amorim S (2016) Statistical and spatial analysis of landslide susceptibility maps with different classification systems. Environ Earth Science 75:1318. https://doi.org/10.1007/s12665-016-6124-1Basofi A, Fariza A, Ahsan AS, Kamal IM (2015) A comparison between natural and head/tail breaks in LSI (landslide susceptibility index) classification for landslide susceptibility mapping: a case study in Ponorogo, East Java, Indonesia. 2015 International Conference on Science in Information Technology, pp 337–342Cantarino I (2013) Elaboración y validación de un modelo jerárquico derivado de SIOSE. Revista de Teledetección 39:5–21Carrara A, Crosta GB, Frattini P (2008) Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology 94(3–4):353–378. https://doi.org/10.1016/j.geomorph.2006.10.033Chacón J, Irigaray C, Fernández T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65(4):341–411Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472COPUT (1998) Lithology, exploitation of industrial rocks and landslide risk in the Valencian Community. Thematic Mapping Series. Department of Public Works of the Valencian Regional GovernmentDrummond C, Holte RC (2006) Cost curves: an improved method for visualizing classifier performance. Mach Learn 65(1):95–130Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environ Geol 51(2):241–256. https://doi.org/10.1007/s00254-006-0322-1Evans IS (1977) The selection of class intervals. Transactions of the Institute of British Geographers. Contemp Cartograph 2(1):98–124. https://doi.org/10.2307/622195Fleiss JL, Levin B, Paik MC (2003) Statistical methods for rates and proportions, Book Series: Wiley Series in Probability and Statistics. John Wiley & Sons. Print ISBN: 9780471526292. doi: https://doi.org/10.1002/0471445428Foody GM (2004) Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogramm Eng Remote Sens 70(5):627–633Fotheringham AS, Brunsdon C, Charlton M (2000) Quantitative geography: perspectives on spatial data analysis. SAGE Publications, Thousand Oaks 270 ppFrattini 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.2009.12.004Geisser S (1998) Comparing two tests used for diagnostic or screening processes. Stat Probability Lett 40:113–119Greiner M, Pfeiffer D, Smith RD (2000) Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 45:23–41Günther A, Reichenbach P, Malet JP, van den Eeckhaut M, Hervás J, Dashwood C, Guzzetti F (2013) Tier-based approaches for landslide susceptibility assessment in Europe. Landslides 10:529–546. https://doi.org/10.1007/s10346-012-0349-1Günther A, Van Den Eeckhaut M, Malet J-P, Reichenbach P, Hervás J (2014) Climate-physiographically differentiated Pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information. Geomorphology 224:69–85Gupta RP, Kanungo DP, Arora MK, Sarkar S (2008) Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps. Int J Appl Earth Obs Geoinf 10(3):330–341. https://doi.org/10.1016/j.jag.2008.01.003Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1–2):166–184. https://doi.org/10.1016/j.geomorph.2006.04.007Hervás J (2017) El inventario de movimientos de ladera de España ALISSA: Metodología y análisis preliminar. In: Alonso E, Corominas J, Hürlimann M (Eds.), Taludes 2017. Proc. IX Simposio Nacional sobre Taludes y Laderas Inestables, Santander, 27–30 June 2017. CIMNE, Barcelona, pp. 629–639Jaedicke C, Van Den Eeckhaut M, Nadim F et al (2014) Identification of landslide hazard and risk ‘hotspots’ in Europe. Bull Eng Geol Environ 73:325. https://doi.org/10.1007/s10064-013-0541-0Jenks GF (1967) The data model concept in statistical mapping. Int Yearbook Cartograph 7:186–190Jiang B (2013) Head/tail breaks: a new classification scheme for data with a heavy-tailed distribution. Prof Geogr 65(3):482–494. https://doi.org/10.1080/00330124.2012.700499Kiang MY (2003) A comparative assessment of classification methods. Decis Support Syst 35(4):441–454. https://doi.org/10.1016/S0167-9236(02)00110-0Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174Langping L, Hengxing L, Changbao G, Yongshuang Z, Quanwen L, Yuming W (2017) A modified frequency ratio method for landslide susceptibility assessment. Landslides 14:727–741. https://doi.org/10.1007/s10346-016-0771-xLee S (2007) Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surf Process Landforms 32:2133–2148. https://doi.org/10.1002/esp.1517Liu C, Frazier P, Kumar L (2007) Comparative assessment of the measures of thematic classification accuracy. Remote Sens Environ 107(4):606–616. https://doi.org/10.1016/j.rse.2006.10.010López-Ratón M, Rodríguez-Álvarez MX, Cadarso-Suárez C, Gude-Sampedro F (2014) Optimal cutpoints: an R package for selecting optimal cutpoints in diagnostic tests. J Stat Softw 61(8):4Malet JP, Puissant A, Mathieu A, Van Den Eeckhaut M, Fressard M (2013) Integrating spatial multi-criteria evaluation and expert knowledge for country-scale landslide susceptibility analysis: application to France. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice. Springer, Berlin. https://doi.org/10.1007/978-3-642-31325-7_40McGee S (2002) Simplifying likelihood ratios. J Gen Intern Med 17:647–650Metz C (1978) Basic principles of ROC analysis. Semin Nucl Med VIII(4):183–198Nadim F, Kjekstad O, Peduzzi P, Herold C, Jaedicke C (2006) Global landslide and avalanche hotspots. Landslides 3:159–173. https://doi.org/10.1007/s10346-006-0036-1Ohlmacher G, Davis J (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69(3–4):331–343. https://doi.org/10.1016/S0013-7952(03)00069-3Powell RL, Matzke N, de Souza C Jr, Clark M, Numata I, Hess LL, Roberts DA (2004) Sources of error accuracy assessment of thematic land-cover maps in the Brazilian Amazon. Remote Sens Environ 90(2):221–234. https://doi.org/10.1016/j.rse.2003.12.007Saaty T (1980) The analytic hierarchy process. McGraw Hill, New YorkSmits PC, Dellepiane SG, Schowengerdt RA (1999) Quality assessment of image classification algorithms for land-cover mapping: a review and proposal for a cost-based approach. Int J Remote Sens 20:1461–1486Stehman SV, Czaplewski RL (1998) Design and analysis of thematic map accuracy assessment: fundamental principles. Remote Sens Environ 64:331–344Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293Van Den Eeckhaut M, Hervás J, Jaedicke C, Malet J-P, Montanarella L, Nadim F (2012) Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data. Landslides 8:357–369Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. Natural hazards. UNESCO, ParisZhu X (2016) GIS for environmental applications. Routledge, Abingdon, p 490Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39(4):561–57