Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS

Journal of Hydrology

Volumn 504, Issue , 2013, Pages 69-79

  Tehrany, Mahyat Shafapour ^a   Pradhan, Biswajeet ^a   Jebur, Mustafa Neamah ^a  

^a UNIVERSITY PUTRA MALAYSIA   (Malaysia)

Author keywords

Decision tree (DT); Ensemble; Flood susceptibility; GIS; Malaysia; Remote sensing

Indexed keywords

DIGITAL ELEVATION MODEL; ENSEMBLE; MALAYSIA; MULTIVARIATE STATISTICAL ANALYSIS; MULTIVARIATE STATISTICAL MODELS; RULE-BASED DECISION TREES; SUSCEPTIBILITY MAPPING; TOPOGRAPHIC WETNESS INDEX;

DATA MINING; DECISION TREES; FORECASTING; GEOGRAPHIC INFORMATION SYSTEMS; MULTIVARIANT ANALYSIS; REMOTE SENSING; STATISTICAL TESTS;

FLOODS;

DATABASE; DECISION MAKING; DIGITAL ELEVATION MODEL; FLOOD CONTROL; FLOOD FORECASTING; FLOODING; GIS; INTEGRATED APPROACH; NUMERICAL MODEL; REMOTE SENSING;

KELANTAN; MALAYSIA; WEST MALAYSIA;

EID: 84885757567     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2013.09.034     Document Type: Article

References (82)

1. Akgun A., Sezer E.A., Nefeslioglu H.A., Gokceoglu C., Pradhan B. An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput. Geosci. 2012, 38:23-34.
2. Ayalew L., Yamagishi H. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 2005, 65:15-31.
3. Bates P.D., Marks K.J., Horritt M.S. Optimal use of high-resolution topographic data in flood inundation models. Hydrol. Process. 2003, 17:537-557.
4. Benediktsson J.O.N.A., Swain P.H., Ersoy O.K. Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Trans. Geosci. Remote Sens. 1990, 28:540-552.
5. Berry M.J., Linoff G. Data Mining Techniques: For Marketing, Sales, and Customer Support 1997, John Wiley & Sons, Inc., New York, NY, USA, pp. 444.
6. Bhaduri K., Wolff R., Giannella C., Kargupta H. Distributed decision-tree induction in peer-to-peer systems. Stat. Anal. Data Min. 2008, 1:85-103.
7. Biggs D., De Ville B., Suen E. A method of choosing multiway partitions for classification and decision trees. J. Appl. Stat. 1991, 18:49-62.
8. Bou Kheir R., Greve M.H., Abdallah C., Dalgaard T. Spatial soil zinc content distribution from terrain parameters: a GIS-based decision-tree model in Lebanon. Environ. Pollut. 2010, 158:520-528.
9. Bronstert A. Floods and climate change: interactions and impacts. Risk Anal. 2003, 23:545-557.
10. Cerra N., Prange Z. Northwest arkansas comprehensive environmental analysis and regional smart growth plan. Inqiry 2012, 13:4-22.
11. Chang N.B., Parvathinathan G., Breeden J.B. Combining GIS with fuzzy multicriteria decision-making for landfill siting in a fast-growing urban region. J. Environ. Manage. 2008, 87:139-153.
12. Chau K.W., Wu C.L., Li Y.S. Comparison of several flood forecasting models in Yangtze River. J. Hydraul. Eng. 2005, 10:485-491.
13. Christensen J.H., Christensen O.B. Climate modelling: severe summertime flooding in Europe. Nature 2003, 421:805-806.
14. Dawson C.W., Abrahart R.J., Shamseldin A.Y., Wilby R.L. Flood estimation at ungauged sites using artificial neural networks. J. Hydrol. 2006, 319:391-409.
15. Degiorgis M., Gnecco G., Gorni S., Roth G., Sanguineti M., Taramasso A.C. Classifiers for the detection of flood-prone areas using remote sensed elevation data. J. Hydrol. 2012, 470-471:302-315.
16. Dixon B. Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. J. Hydrol. 2005, 309:17-38.
17. Fernández D.S., Lutz M.A. Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Eng. Geol. 2010, 111:90-98.
18. Fortin J.P., Turcotte R., Massicotte S., Moussa R., Fitzback J., Villeneuve J.P. Distributed watershed model compatible with remote sensing and GIS data. II: Application to Chaudière watershed. J. Hydrol. Eng. 2001, 6:100-108.
19. García-Pintado J., Neal J.C., Mason D.C., Dance S.L., Bates P.D. Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling. J. Hydrol. 2013, 495:252-266.
20. Ghalkhani H., Golian S., Saghafian B., Farokhnia A., Shamseldin A. Application of surrogate artificial intelligent models for real-time flood routing. Water. Environ. J. 2012,
21. Hostache R., Lai X., Monnier J., Puech C. Assimilation of spatially distributed water levels into a shallow-water flood model. Part II: Use of a remote sensing image of Mosel River. J. Hydrol. 2010, 390:257-268.
22. Hostache, R., Chini, M., Matgen, P., Giustarini, L., 2013. A new automatic SAR-based flood mapping application hosted on the European Space Agency's grid processing on demand fast access to imagery environment. In: EGUGA Conference Abstracts, Vienna, Austria. p. 13927.
23. Islam, M.M., Sado, K., Owe, M., Brubaker, K., Ritchie, J., Rango, A., 2001. Flood damage and management modelling using satellite remote sensing data with GIS: case study of Bangladesh. IAHS Publication, pp. 455-457.
24. Jayakrishnan R., Srinivasan R., Santhi C., Arnold J.G. Advances in the application of the SWAT model for water resources management. Hydrol. Process. 2005, 19:749-762.
25. Jonkman S.N. Global perspectives on loss of human life caused by floods. Nat. Hazards. 2005, 34:151-175.
26. Kia M.B., Pirasteh S., Pradhan B., Mahmud A.R., Sulaiman W.N.A., Moradi A. An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ. Earth Sci. 2012, 67:251-264.
27. Digital Elevation Models and GIS for Watershed Modelling and Flood Prediction - A Case Study of Accra Ghana, Appropriate Technologies for Environmental Protection in the Developing World 2009, 325-332. Springer. D.D. Konadu, C. Fosu (Eds.).
28. Kron, W., 2002. Keynote lecture: Flood risk=hazard×exposure×vulnerability. In: Proceedings of the Flood Defence. Scieence Press, New York Ltd., ISBN 1-880132-54-0.
29. Kusiak A., Li M., Zhang Z. A data-driven approach for steam load prediction in buildings. Appl. Energy 2010, 87:925-933.
30. Lee S., Park I. Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines. J. Environ. Manage. 2013, 127:166-176.
31. Lee S., Pradhan B. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 2007, 4:33-41.
32. Lee, M.J., Kang, J.E., Jeon, S., 2012. Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS). IEEE, pp. 895-898.
33. Li X.H., Zhang Q., Shao M., Li Y.L. A comparison of parameter estimation for distributed hydrological modelling using automatic and manual methods. Adv. Mater. Res. 2012, 356:2372-2375.
34. Liu, Y.B., De Smedt, F., 2004. WetSpa extension, a GIS-based hydrologic model for flood prediction and watershed management. Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel. Vrije Universiteit Brussel: Brussels.
35. Liu Y.B., De Smedt F. Flood modeling for complex terrain using GIS and remote sensed information. Water Resour. Manage. 2005, 19:605-624.
36. Maier H.R., Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Modell. Softw. 2000, 15:101-124.
37. Matgen P., Schumann G., Henry J.B., Hoffmann L., Pfister L. Integration of SAR-derived river inundation areas, high-precision topographic data and a river flow model toward near real-time flood management. Int. J. Appl. Earth Obs. Geoinf. 2007, 9:247-263.
38. Matori, A.N., 2012. Detecting Flood susceptible areas using GIS-based analytic hierarchy process. In: International Conference on Future Environment and Energy, Singapore.
39. McFadden D. Conditional logit analysis of qualitative choice behavior. Frontiers in Econometrics 1974, 105-142. Academic Press, New York. P. Zarembka (Ed.).
40. Merz B., Kreibich H., Thieken A., Schmidtke R. Estimation uncertainty of direct monetary flood damage to buildings. Nat. Hazard. Earth Sys. 2004, 4:153-163.
41. Merz B., Thieken A.H., Gocht M. Flood Risk Mapping at the Local Scale: Concepts and Challenges, Flood Risk Management in Europe 2007, Springer, Netherlands, pp. 231-251.
42. Murthy S.K. Automatic construction of decision trees from data: A multi-disciplinary survey. Data Min. Knowl. Disc. 1998, 2:345-389.
43. Myles A.J., Feudale R.N., Liu Y., Woody N.A., Brown S.D. An introduction to decision tree modeling. J. Chemometr. 2004, 18:275-285.
44. Oh H.J., Pradhan B. Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput. Geosci. 2011, 37:1264-1276.
45. Ohlmacher G.C., Davis J.C. Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng. Geol. 2003, 69:331-343.
46. Opolot E. Application of remote sensing and geographical information systems in flood management: a review. Res. J. Appl. Sci. Eng. Technol. 2013, 6:1884-1894.
47. Pal M., Mather P.M. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens. Environ. 2003, 86:554-565.
48. Pourghasemi H.R., Pradhan B., Gokceoglu C. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat. Hazards 2012, 63:965-996.
49. Pourghasemi H.R., Mohammadi M., Pradhan B. Landslide susceptibility mapping using index of entropy and conditional probability models at Safarood Basin, Iran. Catena 2012, 97:71-84.
50. Pourghasemi H.R., Moradi H.R., Fatemi Aghda S.M., Gokceoglu C., Pradhan B. GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi criteria evaluation models (North of Tehran, Iran). Arab. J. Geosci. 2013,
51. Pourghasemi H.R., Pradhan B., Gokceoglu C., Moezzi K.D. A comparative assessment of prediction capabilities of Dempster-Shafer and weights of evidence models in landslide susceptibility mapping using GIS. Geomatics, Natural Hazards and Risk 2013, 4:93-118.
52. Pradhan B. Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Cent. Eur. J. Geosci. 2009, 1:120-129.
53. Pradhan B. Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J. Spatial. Hydrol. 2010, 9.
54. Pradhan B. Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J. Indian Soc. Remote Sens. 2010, 38:301-320.
55. Pradhan B. 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. 2013, 51:350-365.
56. Pradhan B., Buchroithner M.F. Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environ. Eng Geosci. 2010, 16:107-126.
57. Pradhan B., Lee S. Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 2010, 7:13-30.
58. Pradhan B., Youssef A.M. A 100-year maximum flood susceptibility mapping using integrated hydrological and hydrodynamic models: Kelantan River Corridor, Malaysia. J. Flood risk. Manage. 2011, 4:189-202.
59. Pradhan B., Shafiee M., Pirasteh S. Maximum flood prone area mapping using RADARSAT images and GIS: Kelantan river basin. Int. J. Geoinform. 2009, 5:11.
60. Pradhan B., Youssef A.M., Varathrajoo R. Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model. Geo-Spatial Inform.Sci. 2010, 13:93-102.
61. Pradhan B., Mansor S., Pirasteh S., Buchroithner M.F. Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model. Int. J. Remote Sens. 2011, 32:4075-4087.
62. Prakash S., Mahesh C., Gairola R.M., Buyantogtokh B. A feasibility of six-hourly rainfall forecast over central India using model output and remote sensing data. Int. J. Hydrol Sci. Technol. 2012, 2:138-152.
63. Roe B.P., Yang H.J., Zhu J., Liu Y., Stancu I., McGregor G. Boosted decision trees as an alternative to artificial neural networks for particle identification. Nucl. Instrum. Methods A 2005, 543:577-584.
64. Rozalis S., Morin E., Yair Y., Price C. Flash flood prediction using an uncalibrated hydrological model and radar rainfall data in a Mediterranean watershed under changing hydrological conditions. J. Hydrol. 2010, 394:245-255.
65. Saito H., Nakayama D., Matsuyama H. Estimation and evaluation of drainage basins with landslides using decision tree technique and ASTER data. J. Jpn. Landslide Soc. 2007, 44:1-14.
66. 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, 109:108-121.
67. Schneevoigt N.J., van der Linden S., Thamm H.P., Schrott L. Detecting alpine landforms from remotely sensed imagery. A pilot study in the Bavarian Alps. Geomorphology 2008, 93:104-119.
68. Shirzadi A., Saro L., Joo O.H., Chapi K. A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran. Nat. Hazards 2012, 64:1639-1656.
69. Smith K., Ward R. Floods: Physical Processes and Human Impacts 1998, Wiley, Chichester, p. 394.
70. Stephens E.M., Bates P.D., Freer J.E., Mason D.C. The impact of uncertainty in satellite data on the assessment of flood inundation models. J. Hydrol. 2012, 414:162-173.
71. Süzen M.L., Doyuran V. A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ. Geol. 2004, 45:665-679.
72. 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. <. http://dx.doi.org/10.1155/2012/974638.
73. Tien Bui D., Pradhan B., Lofman O., Revhaug I., Dick O.B. Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput. Geosci. 2012, 45:199-211.
74. Tien Bui D., Pradhan B., Lofman O., Revhaug I., Dick O.B. 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 2012, 96:28-40.
75. Tilmant A., Vanclooster M., Duckstein L., Persoons E. Comparison of fuzzy and nonfuzzy optimal reservoir operating policies. J. Water Resour. Plan. Manage. 2002, 128:390-398.
76. Townsend P.A., Walsh S.J. Modeling floodplain inundation using an integrated GIS with radar and optical remote sensing. Geomorphology 1998, 21:295-312.
77. Ture M., Tokatli F., Kurt Omurlu I. The comparisons of prognostic indexes using data mining techniques and Cox regression analysis in the breast cancer data. Expert Syst. Appl. 2009, 36:8247-8254.
78. Wan Z., Wang G., Sun B. A hybrid intelligent algorithm by combining particle swarm optimization with chaos searching technique for solving nonlinear bilevel. Swarm and Evolutionary Computation 2012, 8:26-32.
79. Witten I.H., Frank E. Data Mining: Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann, San Francisco, p. 525.
80. Xu M., Watanachaturaporn P., Varshney P.K., Arora M.K. Decision tree regression for soft classification of remote sensing data. Remote Sens. Environ. 2005, 97:322-336.
81. Yilmaz I. GIS based susceptibility mapping of karst depression in gypsum: a case study from Sivas basin (Turkey). Eng. Geol. 2007, 90:89-103.
82. Youssef A.M., Pradhan B., Hassan A.M. Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environ. Earth Sci. 2011, 62:611-623.