A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area

Geotechnical and Geological Engineering

Volumn 34, Issue 6, 2016, Pages 1807-1824

  Pham, Binh Thai ^a,b   Tien Bui, Dieu ^c   Dholakia, M B ^a   Prakash, Indra ^d   Pham, Ha Viet ^e  

^a GUJARAT TECHNOLOGICAL UNIVERSITY   (India)

^b University of Transport Technology   (Viet Nam)

^c UNIVERSITY OF SOUTH EASTERN NORWAY   (Norway)

^d Government of India)   (India)

^e Vietnam Institute of Geosciences and Mineral Resources   (Viet Nam)

Author keywords

Landslides; GIS; Least square support vector machine; Multiclass alternating decision tree; Vietnam; Yen Bai

Indexed keywords

DECISION TREES; FORECASTING; GEOGRAPHIC INFORMATION SYSTEMS; HAZARDS; LAND USE; LANDSLIDES; LEARNING ALGORITHMS; LEARNING SYSTEMS; LITHOLOGY; LOCATION; RAIN; STORMS; SUPPORT VECTOR MACHINES;

ALTERNATING DECISION TREES; LANDSLIDE HAZARD ASSESSMENT; LANDSLIDE SUSCEPTIBILITY ASSESSMENTS; LEAST SQUARE SUPPORT VECTOR MACHINES; RAINFALL INDUCED LANDSLIDES; RECEIVER OPERATING CHARACTERISTIC CURVES; VIET NAM; YEN BAI;

DECISION MAKING;

COMPARATIVE STUDY; GIS; HAZARD MANAGEMENT; LANDSLIDE; LEAST SQUARES METHOD; PRECIPITATION INTENSITY; SPATIAL ANALYSIS; SUPPORT VECTOR MACHINE; TROPICAL CYCLONE;

VIET NAM; YEN BAI;

EID: 84958780491     PISSN: 09603182     EISSN: 15731529     Source Type: Journal    
DOI: 10.1007/s10706-016-9990-0     Document Type: Article

References (79)

1. Ahlheim M et al (2008) Landslides in mountainous regions of Northern Vietnam: causes, protection strategies and the assessment of economic losses. University of Hohenheim, Germany, pp 1–19
2. Akgun 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
3. Aksoy B, Ercanoglu M (2012) Landslide identification and classification by object-based image analysis and fuzzy logic: an example from the Azdavay region (Kastamonu, Turkey). Comput Geosci 38:87–98
4. Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44
5. Alizadehsani R et al (2013) A data mining approach for diagnosis of coronary artery disease. Comput Methods Programs Biomed 111:52–61
6. Alkhasawneh MS, Ngah UK, Tay LT, Isa NAM, Al-Batah MS (2014) Modeling and testing landslide hazard using decision tree. J Appl Math 2014:1–9
7. Althuwaynee OF, Pradhan B, Park H-J, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. CATENA 114:21–36
8. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31
9. Baylar A, Hanbay D, Batan M (2009) Application of least square support vector machines in the prediction of aeration performance of plunging overfall jets from weirs. Expert Syst Appl 36:8368–8374
10. Beguería S (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards 37:315–329
11. Beucher A, Österholm P, Martinkauppi A, Edén P, Fröjdö S (2013) Artificial neural network for acid sulfate soil mapping: application to the Sirppujoki River catchment area, south-western Finland. J Geochem Explor 125:46–55
12. Cawley GC, Talbot NL (2004) Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Netw 17:1467–1475
13. Choi J, Oh H-J, Lee H-J, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23
14. Chu W, Ong CJ, Keerthi SS (2005) An improved conjugate gradient scheme to the solution of least squares SVM. IEEE Trans Neural Netw 16:498–501
15. CNN (2010) 46 killed in landslides, floods in Vietnam. http://www.cnn.com/2010/WORLD/asiapcf/10/20/vietnam.death.toll.floods/
16. Conoscenti C, Angileri S, Cappadonia C, Rotigliano E, Agnesi V, Märker M (2014) Gully erosion susceptibility assessment by means of GIS-based logistic regression: a case of Sicily (Italy). Geomorphology 204:399–411
17. Das I, Stein A, Kerle N, Dadhwal VK (2012) Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models. Geomorphology 179:116–125
18. DeLeo JM (1993) Receiver operating characteristic laboratory (ROCLAB): software for developing decision strategies that account for uncertainty. In: Proceedings of second international symposium on uncertainty modeling and analysis, IEEE, pp 318–325
19. Devkota KC et al (2013) Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Nat Hazards 65:135–165
20. Faraji Sabokbar H, Shadman Roodposhti M, Tazik E (2014) Landslide susceptibility mapping using geographically-weighted principal component analysis. Geomorphology 226:15–24
21. Fletcher R (1987) Practical methods of optimization. Wiley, Hoboken
22. Freund Y, Mason L (1999) The alternating decision tree learning algorithm. icml 99:124–133
23. Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28:337–407
24. Gorsevski PV, Gessler PE, Foltz RB, Elliot WJ (2006) Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS 10:395–415
25. Grekousis G, Manetos P, Photis YN (2013) Modeling urban evolution using neural networks, fuzzy logic and GIS: the case of the Athens metropolitan area. Cities 30:193–203
26. Hanbay D, Baylar A, Batan M (2009) Prediction of aeration efficiency on stepped cascades by using least square support vector machines. Expert Syst Appl 36:4248–4252
27. Holmes G, Pfahringer B, Kirkby R, Frank E, Hall M (2002) Multiclass alternating decision trees. In: Elomaa T, Mannila H, Toivonen H (eds) Machine learning: ECML 2002. Springer, London, pp 161–172
28. Hungr O, Salgado F, Byrne P (1989) Evaluation of a three-dimensional method of slope stability analysis. Can Geotech J 26:679–686
29. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680
30. Lai KK, Yu L, Zhou L, Wang S (2006) Credit risk evaluation with least square support vector machine. In: Wang G et al (eds) Rough sets and knowledge technology. Springer, Berlin, pp 490–495
31. Lee S, Park I, Koo BJ, Ryu J-H, Choi J-K, Woo HJ (2013) Macrobenthos habitat potential mapping using GIS-based artificial neural network models. Mar Pollut Bull 67:177–186
32. Minh BH (2012) Landslides, floods kill 29 in Vietnam-media. http://www.reuters.com/article/us-vietnam-disaster-toll-idUSBRE88702F20120908
33. Mitra V, Wang C-J, Banerjee S (2007) Text classification: a least square support vector machine approach. Appl Soft Comput 7:908–914
34. Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. J Asian Earth Sci 61:221–236
35. Morgado P, Gomes E, Costa N (2014) Competing visions? Simulating alternative coastal futures using a GIS-ANN web application. Ocean Coast Manag 101(Part B):79–88
36. Nampak H, Pradhan B, Manap MA (2014) Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol 513:283–300
37. NCEP (2014) Global Weather data for SWAT. http://globalweather.tamu.edu
38. Nefeslioglu H, Sezer E, Gokceoglu C, Bozkir A, Duman T (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Math Probl Eng 2010:1–15
39. Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: sultan Mountains, SW Turkey. J Asian Earth Sci 64:180–197
40. Pham BT, Tien Bui D, Indra P, Dholakia MB (2015a) Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS–based statistical approach of frequency ratio method. Int J Eng Res Technol 4:338–344
41. Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia MB (2015b) Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol 1-19
42. Phong B (2014) Vietnam has 10,000 sites with landslide risks. http://english.vietnamnet.vn/fms/environment/111912/vietnam-has-10-000-sites-with-landslide-risks.html
43. Polat K, Güneş S (2007) Breast cancer diagnosis using least square support vector machine. Digit Signal Proc 17:694–701
44. Poudyal CP, Chang C, Oh H-J, Lee S (2010) Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environ Earth Sci 61:1049–1064
45. Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996
46. Pradhan B (2011) Manifestation of an advanced fuzzy logic model coupled with Geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modelling. Environ Ecol Stat 18:471–493
47. Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365
48. Pradhan B, Lee S (2010) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60:1037–1054
49. Pradhan B, Abokharima MH, Jebur MN, Tehrany MS (2014) Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards 73:1019–1042
50. Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian J Geosci 7:725–742
51. Samui P, Karthikeyan J (2013) Determination of liquefaction susceptibility of soil: a least square support vector machine approach. Int J Numer Anal Meth Geomech 37:1154–1161
52. Saunders C, Gammerman A, Vovk V (1998) Ridge regression learning algorithm in dual variables. In: (ICML-1998) Proceedings of the 15th international conference on machine learning. Morgan Kaufmann, pp 515–521
53. Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: klang valley,Malaysia. Expert Syst Appl 38:8208–8219. doi:10.1016/j.eswa.2010.12.167
54. Shahabi H, Khezri S, Ahmad BB, Hashim M (2014) Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. CATENA 115:55–70
55. Srimani P, Koti MS (2013) Medical diagnosis using ensemble classifiers-a novel machine-learning approach. J Adv Comput 1:9–27
56. Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9:293–300
57. Suykens JA, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J, Suykens J, Van Gestel T (2002) Least squares support vector machines, vol 4. World Scientific, Singapore
58. Tien Bui D (2012) Modeling of rainfall-induced landslide hazard for the Hoa Binh province of Vietnam. Ph.D Thesis, Norwegian University of Life Sciences
59. Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012a) Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Naive Bayes Models. Math Probl Eng 2012:1–26
60. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) Landslide susceptibility assessment in the Hoa Binh province of Vietnam: a comparison of the Levenberg–Marquardt and Bayesian regularized neural networks. Geomorphology 171–172:12–29
61. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012c) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211
62. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012d) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40
63. Tien Bui D, Pradhan B, Revhaug I, Trung Tran C (2014) A comparative assessment between the application of fuzzy unordered rules induction algorithm and J48 decision tree models in spatial prediction of shallow landslides at Lang Son City, Vietnam. Remote Sens Appl Environ Res 87–111
64. Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2015) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 1–18. doi:10.1007/s10346-015-0557-6
65. Umar Z, Pradhan B, Ahmad A, Jebur MN, Tehrany MS (2014) Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. CATENA 118:124–135
66. Van Gestel T, Baesens B, Suykens J, Espinoza M, Baestaens D-E, Vanthienen J, De Moor B (2003) Bankruptcy prediction with least squares support vector machine classifiers. In: Proceedings of 2003 IEEE international conference on computational intelligence for financial engineering, 2003, IEEE, pp 1–8
67. Van Westen C, Asch T, Soeters R (2006) Landslide hazard and risk zonation-why is it still so difficult. Bull Eng Geol Environ 65:67–184
68. Van TT et al (2006) Investigation and assessment of the current status and potential of landslides in some sections of the Ho Chi Minh Road, National Road 1A and proposed remedial measures to prevent landslides from threat of safety of people, property, and infrastructure. Vietnam Institute of Geosciences and Mineral Resources, Hanoi
69. Vapnik VN, Vapnik V (1998) Statistical learning theory, vol 1. Wiley, New York
70. Wisaeng K (2013) A comparison of different classification techniques for bank direct marketing. Int J Soft Comput Eng (IJSCE) 3:116–119
71. Wu Q, Liu S-y DuZ (2007) Fuzzy least square support vector machines for regression. J Xidian Univ 5:021
72. Xavier-de-Souza S, Suykens JA, Vandewalle J, Bollé D (2010) Coupled simulated annealing. IEEE Trans Syst Man Cybern Part B Cybern 40:320–335
73. Xu C, Dai F, Xu X, Lee YH (2012a) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed. China Geomorphology 145–146:70–80
74. Xu C, Xu X, Dai F, Xiao J, Tan X, Yuan R (2012b) Landslide hazard mapping using GIS and weight of evidence model in Qingshui river watershed of 2008 Wenchuan earthquake struck region. J Earth Sci 23:97–120
75. 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
76. Yeon Y-K, Han J-G, Ryu KH (2010) Landslide susceptibility mapping in Injae, Korea, using a decision tree. Eng Geol 116:274–283
77. Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat–Turkey). Comput Geosci 35:1125–1138
78. Zhang N, Williams C, Behera P (2014) Water quantity prediction using least squares support vector machines (LS-SVM) method. J Syst Cybern Inform 2:53–58
79. Zhao Y, Zhang Y (2008) Comparison of decision tree methods for finding active objects. Adv Space Res 41:1955–1959