Commenced in January 2007
Paper Count: 31103
Integrating Decision Tree and Spatial Cluster Analysis for Landslide Susceptibility Zonation
Abstract:Landslide susceptibility map delineates the potential zones for landslide occurrence. Previous works have applied multivariate methods and neural networks for mapping landslide susceptibility. This study proposed a new approach to integrate decision tree model and spatial cluster statistic for assessing landslide susceptibility spatially. A total of 2057 landslide cells were digitized for developing the landslide decision tree model. The relationships of landslides and instability factors were explicitly represented by using tree graphs in the model. The local Getis-Ord statistics were used to cluster cells with high landslide probability. The analytic result from the local Getis-Ord statistics was classed to create a map of landslide susceptibility zones. The map was validated using new landslide data with 482 cells. Results of validation show an accuracy rate of 86.1% in predicting new landslide occurrence. This indicates that the proposed approach is useful for improving landslide susceptibility mapping.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1077482Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1544
 EM-DAT, The OFDA/CRED International Disaster Database. 2008, Universit'e Catholique de Louvain - Brussels - Belgium.
 Guzzetti, F., P. Reichenbach, F. Ardizzone, M. Cardinali, and M. Galli, "Estimating the quality of landslide susceptibility models," Geomorphology. vol 1-2: pp. 166-184, 2006.
 Carrara, A., "Multivariate models for landslide hazard evaluation," Mathematical Geology. vol 3: pp. 403-426, 1983.
 Carrara, A., M. Cardinali, R. Detti, F. Guzzetti, V. Pasqui, and P. Reichenbach, "Gis Techniques and Statistical-Models in Evaluating Landslide Hazard," Earth Surface Processes and Landforms. vol 5: pp. 427-445, 1991.
 Reger, J.P., "Discriminant-Analysis as a Possible Tool in Landslide Investigations," Earth Surface Processes and Landforms. vol 3: pp. 267-273, 1979.
 Dai, F.C. and C.F. Lee, "Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong," Geomorphology. vol 3-4: pp. 213-228, 2002.
 Lee, S., "Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data," International Journal of Remote Sensing. vol 7: pp. 1477-1491, 2005.
 van Den Eeckhaut, M., T. Vanwalleghem, J. Poesen, G. Govers, G. Verstraeten, and L. Vandekerckhove, "Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium)," Geomorphology. vol 3-4: pp. 392-410, 2006.
 Yesilnacar, E. and T. Topal, "Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)," Engineering Geology. vol 3-4: pp. 251-266, 2005.
 Chang, K.T., S.H. Chiang, and M.L. Hsu, "Modeling typhoon-and earthquake-induced landslides in a mountainous watershed using logistic regression," Geomorphology. vol 3-4: pp. 335-347, 2007.
 Melchiorre, C., M. Matteucci, A. Azzoni, and A. Zanchi, "Artificial neural networks and cluster analysis in landslide susceptibility zonation," Geomorphology. vol 3-4: pp. 379-400, 2008.
 Ermini, L., F. Catani, and N. Casagli, "Artificial Neural Networks applied to landslide susceptibility assessment," Geomorphology. vol 1-4: pp. 327-343, 2005.
 van Asch, T.W.J., J.P. Malet, L.P.H. van Beek, and D. Amitrano, "Techniques, advances, problems and issues in numerical modelling of landslide hazard," Bulletin de la Societe Geologique de France. vol 2: pp. 65-88, 2007.
 van Westen, C.J., A.C. Seijmonsbergen, and F. Mantovani, "Comparing Landslide Hazard Maps," Natural Hazards. vol 2: pp. 137-158, 1999.
 Lee, S., J.H. Ryu, M.J. Lee, and J.S. Won, "The Application of Artificial Neural Networks to Landslide Susceptibility Mapping at Janghung, Korea," Mathematical Geology. vol 2: pp. 199-220, 2006.
 Neaupane, K.M. and S.H. Achet, "Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya," Engineering Geology. vol 3-4: pp. 213-226, 2004.
 Paliwal, M. and U. Kumar, "Neural networks and statistical techniques: A review of applications," Expert Syst Appl. vol 1: pp. 2-17, 2009.
 Brieman, L., J.H. Friedman, R.A. Olshen, and C.J. Stone, "Classification and Regression Trees," Wadsworth Inc. vol, 1984.
 Camp, N.J. and M.L. Slattery, "Classification tree analysis: a statistical tool to investigate risk factor interactions with an example for colon cancer (United States)," Cancer Cause Control. vol 9: pp. 813-823, 2002.
 De'ath, G. and K.E. Fabricius, "Classification and Regression Trees: A Powerful Yet Simple Technique for Ecological Data Analysis," Ecology. vol 11: pp. 3178-3192, 2000.
 Pal, M. and P.M. Mather, "An assessment of the effectiveness of decision tree methods for land cover classification," Remote Sens Environ. vol 4: pp. 554-565, 2003.
 Friedl, M.A. and C.E. Brodley, "Decision tree classification of land cover from remotely sensed data," Remote Sens Environ. vol 3: pp. 399-409, 1997.
 Scull, P., J. Franklin, and O.A. Chadwick, "The application of classification tree analysis to soil type prediction in a desert landscape," Ecological Modelling. vol 1: pp. 1-15, 2005.
 Lagacherie, P. and S. Holmes, "Addressing geographical data errors in a classification tree for soil unit prediction," International Journal of Geographical Information Science. vol 2: pp. 183-198, 1997.
 Wu, J., et al., "Exploratory spatial data analysis for the identification of risk factors to birth defects," BMC Public Health. vol 1: pp. 23, 2004.
 Getis, A. and J. Ord, "The analysis of spatial association by use of distance statistics," Geographical analysis. vol 3: pp. 189-206, 1992.
 Ord, J. and A. Getis, "Local spatial autocorrelation statistics: distributional issues and an application," Geographical analysis. vol 4: pp. 286-306, 1995.