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IJMLC 2014 Vol. 4(5): 450-457 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.453

Analytic Hierarchy Process (AHP) in Spatial Modeling for Floodplain Risk Assessment

Generino P. Siddayao, Sony E. Valdez, and Proceso L. Fernandez

Abstract—Climate change is a phenomenon that is forcing the world to adapt to a different environment. In this study, Analytical Hierarchy Process (AHP) method is combined with a Geographical Information System (GIS) for flood risk analysis and evaluation in the town of Enrile, a flood-prone area located in northern Philippines. Expert opinions, together with geographical, statistical and historical data, were collected and then processed through fuzzy membership. The AHP results showed the relative weights of three identified flood risk factors, and these results were validated to be consistent, using a standard consistency index. Using the Quantum GIS software, the factor weights from the AHP were incorporated to produce a map that is color-coded representing 5 levels of estimated flood risks. Using such a GIS weighted overlay analysis map as guide, local councils and other stakeholders can act to prepare for potential flooding when the rains come or, better yet, proactively promote appropriate land-use policy that will minimize threat to lives due to flooding.

Index Terms—Analytic hierarchy process (AHP), decision support system, geographic information system (GIS), land-use policy.

G. P. Siddayao is with the Cagayan State University, College of Information and Computing Sciences, Tuguegarao City, Philippines 3500 (e-mail: genersiddayao@gmail.com).
S. E. Valdez is with the Agoo Computer College, Agoo, La Union, Philippines (e-mail: shunyvaldez@gmail.com).
P. L. Fernandez, Jr. is with the Ateneo de Manila University, Quezon City, Philippines (e-mail: pfernandez@ateneo.edu).


Cite: Generino P. Siddayao, Sony E. Valdez, and Proceso L. Fernandez, "Analytic Hierarchy Process (AHP) in Spatial Modeling for Floodplain Risk Assessment," International Journal of Machine Learning and Computing vol. 4, no. 5, pp. 450-457, 2014.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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