Integrating Decision Tree and Spatial Cluster Analysis for Landslide Susceptibility Zonation
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.
[1] EM-DAT, The OFDA/CRED International Disaster Database. 2008,
Universit'e Catholique de Louvain - Brussels - Belgium.
[2] 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.
[3] Carrara, A., "Multivariate models for landslide hazard evaluation,"
Mathematical Geology. vol 3: pp. 403-426, 1983.
[4] 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.
[5] Reger, J.P., "Discriminant-Analysis as a Possible Tool in Landslide
Investigations," Earth Surface Processes and Landforms. vol 3: pp.
267-273, 1979.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] Ermini, L., F. Catani, and N. Casagli, "Artificial Neural Networks applied
to landslide susceptibility assessment," Geomorphology. vol 1-4: pp.
327-343, 2005.
[13] 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.
[14] van Westen, C.J., A.C. Seijmonsbergen, and F. Mantovani, "Comparing
Landslide Hazard Maps," Natural Hazards. vol 2: pp. 137-158, 1999.
[15] 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.
[16] 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.
[17] Paliwal, M. and U. Kumar, "Neural networks and statistical techniques: A
review of applications," Expert Syst Appl. vol 1: pp. 2-17, 2009.
[18] Brieman, L., J.H. Friedman, R.A. Olshen, and C.J. Stone, "Classification
and Regression Trees," Wadsworth Inc. vol, 1984.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] Getis, A. and J. Ord, "The analysis of spatial association by use of
distance statistics," Geographical analysis. vol 3: pp. 189-206, 1992.
[27] Ord, J. and A. Getis, "Local spatial autocorrelation statistics:
distributional issues and an application," Geographical analysis. vol 4: pp.
286-306, 1995.
[1] EM-DAT, The OFDA/CRED International Disaster Database. 2008,
Universit'e Catholique de Louvain - Brussels - Belgium.
[2] 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.
[3] Carrara, A., "Multivariate models for landslide hazard evaluation,"
Mathematical Geology. vol 3: pp. 403-426, 1983.
[4] 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.
[5] Reger, J.P., "Discriminant-Analysis as a Possible Tool in Landslide
Investigations," Earth Surface Processes and Landforms. vol 3: pp.
267-273, 1979.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] Ermini, L., F. Catani, and N. Casagli, "Artificial Neural Networks applied
to landslide susceptibility assessment," Geomorphology. vol 1-4: pp.
327-343, 2005.
[13] 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.
[14] van Westen, C.J., A.C. Seijmonsbergen, and F. Mantovani, "Comparing
Landslide Hazard Maps," Natural Hazards. vol 2: pp. 137-158, 1999.
[15] 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.
[16] 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.
[17] Paliwal, M. and U. Kumar, "Neural networks and statistical techniques: A
review of applications," Expert Syst Appl. vol 1: pp. 2-17, 2009.
[18] Brieman, L., J.H. Friedman, R.A. Olshen, and C.J. Stone, "Classification
and Regression Trees," Wadsworth Inc. vol, 1984.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] Getis, A. and J. Ord, "The analysis of spatial association by use of
distance statistics," Geographical analysis. vol 3: pp. 189-206, 1992.
[27] Ord, J. and A. Getis, "Local spatial autocorrelation statistics:
distributional issues and an application," Geographical analysis. vol 4: pp.
286-306, 1995.
@article{"International Journal of Earth, Energy and Environmental Sciences:60581", author = "Chien-Min Chu and Bor-Wen Tsai and Kang-Tsung Chang", title = "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.", keywords = "Landslide susceptibility Zonation, Decision treemodel, Spatial cluster, Local Getis-Ord statistics.", volume = "3", number = "11", pages = "376-5", }
