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Improving Intelligent Decision Making in Urban Planning: Using Machine Learning Algorithms

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  • Abderrazak Khediri

    (Laboratory of Mathematics, Informatics, and Systems (LAMIS), University of Larbi Tebessi, Tebessa, Algeria)

  • Mohamed Ridda Laouar

    (Laboratory of Mathematics, Informatics, and Systems (LAMIS), University of Larbi Tebessi, Tebessa, Algeria)

  • Sean B. Eom

    (Southeast Missouri State University, USA)

Abstract

Generally, decision making in urban planning has progressively become difficult due to the uncertain, convoluted, and multi-criteria nature of urban issues. Even though there has been a growing interest to this domain, traditional decision support systems are no longer able to effectively support the decision process. This paper aims to elaborate an intelligent decision support system (IDSS) that provides relevant assistance to urban planners in urban projects. This research addresses the use of new techniques that contribute to intelligent decision making: machine learning classifiers, naïve Bayes classifier, and agglomerative clustering. Finally, a prototype is being developed to concretize the proposition.

Suggested Citation

  • Abderrazak Khediri & Mohamed Ridda Laouar & Sean B. Eom, 2021. "Improving Intelligent Decision Making in Urban Planning: Using Machine Learning Algorithms," International Journal of Business Analytics (IJBAN), IGI Global, vol. 8(3), pages 40-58, July.
    • Handle: RePEc:igg:jban00:v:8:y:2021:i:3:p:40-58
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