Landslide susceptibility mapping in Injae, Korea, using a decision tree

Engineering Geology

Volumn 116, Issue 3-4, 2010, Pages 274-283

  Yeon, Young Kwang ^a   Han, Jong Gyu ^a   Ryu, Keun Ho ^b  

^a Korea Institute of Geosceince and Mineral Resources   (South Korea)

^b Chungbuk National University   (South Korea)

Author keywords

C4.5 algorithm; Decision tree; Korea; Landslide predictability; Spatial events

Indexed keywords

C4.5 ALGORITHM; CLASS DISTRIBUTIONS; CLASSIFICATION ALGORITHM; CROSS VALIDATION; EFFECTIVE THICKNESS; HEAVY RAINS; KOREA; LANDSLIDE SUSCEPTIBILITY; LANDSLIDE SUSCEPTIBILITY MAPPING; LOCAL MAP; MINING CLASSIFICATION; QUANTITATIVE ASSESSMENTS; SOIL TEXTURES; SPATIAL EVENTS;

ALGORITHMS; DATA MINING; DECISION TREES;

LANDSLIDES;

ACCURACY ASSESSMENT; ALGORITHM; DATA MINING; DECISION ANALYSIS; GEOLOGICAL MAPPING; LANDSLIDE; SMOOTHING; SPATIAL ANALYSIS; TOPOGRAPHIC MAPPING;

SOUTH KOREA;

EID: 78149413054     PISSN: 00137952     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enggeo.2010.09.009     Document Type: Article

References (30)

1. Aleotti P., Chowdhury R. Landslide hazard assessment: summary review and new perspectives. Bull Eng Geo Environ 1999, 58:21-44.
2. Atkinson P.M., Massari R. Generalized linear modeling of susceptibility to landsliding in the central Apennines, Italy. Computer & Geosciences 1998, 24:373-385.
3. Berry M.J.A., Linoff G. Data Mining Techniques: For Marketing, Sales, and Customer Support 1997, John Wiley & Sons.
4. Bonham-Carter G.F. Geographic information system for geoscientist, modeling with GIS 1994, Pergamon Press, Oxford, 398.
5. Breiman L., Friedman J., Olshen R., Stone C. Classification and Regression Trees, Chapman & Hal 1984, Wadsworth, Inc, New York.
6. Chung C.F., Fabbri A.G. Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering & Remote Sensing (PE&RS) 1999, 65(12):1388-1399.
7. Cussents J. Bayes and psudo-bayes estimates of conditional probabilities and their reliabilities. Proceedings of European Conference on Machine Learning 1993.
8. Dai F.C., Lee C.F.J., Li J., Xu Z.W. Assessment of landslide susceptibility on the natural terrain of Lantau Island. Hong Kong, Environmental Geology 2001, 40:381-391.
9. Donati L., Turrini M.C. An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy). Eng. Geol. 2002, 63:277-289.
10. Ermini L., Catani L., Casagli N. Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology 2004, 66(1-4):327-343.
11. Ferri C., Flach P.A., Hernndez-Orallo J. Improving the AUC of probabilistic estimation trees. Proc. of the 14th European Conf. on Machine Learning 2003, 121-132.
12. Gómez H., Kavzoglu T. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River. Basin, Venezuela. Eng. Geol. 2005, 78:11-27.
13. Guzzetti F., Reichenbach P., Ardizzone F., Cardinali M., Galli M. Estimating the quality of landslide susceptibility models. Geomorphology 2006, 81:166-184.
14. Lee S., Chol U.C. Development of GIS-based geological hazard information system and its application for landslide analysis in Korea. Geosci. J. 2003, 7:243-252.
15. Lee S., Min K. Statistical analysis of landslide susceptibility at Yongin. Korea, Environmental Geology 2001, 40:1095-1113.
16. Lee S., Sambath T. Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ. Geol. 2006, 50:847-855.
17. Lee S., Ryu J.H., Won J.S., Park H.J. Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng. Geol. 2004, 71(3-4):289-302.
18. Luzi L., Pergalani F., Terlien M.T.J. Slope vulnerability to earthquakes at subregional scale, using probabilistic techniques and geographic information systems. Eng. Geol. 2000, 58:313-336.
19. Melchiorre C., Matteucci M., Azzoni A., Zanchi A. Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 2008, 94:379-400.
20. Nefeslioglu H., Duman T., Durmaz S. Landslide susceptibility mapping for a part of tectonic Kelkit Valley., Eastern Black Sea region of Turkey). Geomorphology 2008, 94:401-418.
21. Nefeslioglu H., Sezer E., Gokceoglu C., Bozkir A., Duman T. Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Mathematical Problems in Engineering 2010, Article ID 901095 2010.
22. Neuhäuser B., Terhorst B. Landslide susceptibility assessment using "weights-of-evidence" applied to a study area at the Jurassic escarpment (SW-Germany). Geomorphology 2007, 86:12-24.
23. Pal M., Mather P.M. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens. Environ. 2003, 86:554-556.
24. Provost F.J., Domingos P. Tree Induction for Probability-based Ranking. Machine Learning Kluwer Academic Publisher 2003, 52(3):199-215.
25. Quinlan J.R. Induction of decision trees. Machine Learning 1986, 1:81-106.
26. Quinlan J.R. C4.5: Programs for Machine Learning, Morgan Kaufmann 1993.
27. Saito H., Nakayama D., Matsuyama H. Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi Mountains, Japan. Geomorphology 2009, 109(3-4):108-121.
28. Swets J.A. Measuring the accuracy of diagnostic systems. Science 1988, 240:1285-1293.
29. Wu X., Kumar V., Quinlan J.R., Ghosh J., Yang Q., Motoda H., MacLachlan G.J., Ng A., Liu B., Yu P.S., Zhou Z.H., Steinbach M., Hand D.J., Steinberg D. Top 10 algorithms in data mining. Knowl. Inf. Syst. 2008, 14(1):1-37.
30. Zadrozny B., Elkan C. Learning and making decisions when costs and probabilities are both unknown. Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2001, 204-213.