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Social-Based Physical Reconstruction Planning in Case of Natural Disaster: A Machine Learning Approach

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Research Challenges in Information Science (RCIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 385))

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Abstract

Natural disasters have several adverse effects on human lives. It is challenging for the governments to tackle these events and to reconstruct damaged areas with minimal budget and time, but still guaranteeing social benefits to the affected population. This article presents an approach of decision-support system for post-disaster re-construction planning of buildings damaged by a natural disaster. The proposed framework determines a set of alternative plans which satisfy all constraints, accommodate political priorities, and guarantee social benefits for the affected population. The determined plans are then provided to public servants that select the plan to implement. The approach is generic and it can be applied to areas of any extension as long as the decision makers share the same goals. We will demonstrate the approach on the L’Aquila city destroyed by an earthquake in 2009.

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Notes

  1. 1.

    Such as, but not limited to, earthquakes, floods, and storms.

  2. 2.

    https://www.economicshelp.org/blog/glossary/social-benefit/.

  3. 3.

    https://pathmind.com/wiki/deep-reinforcement-learning.

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Acknowledgements

This PhD project is part of Terrotori Aperti project funded by Fondo Territori Lavoro e Conoscenza CGIL, CSIL and UIL.

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Authors and Affiliations

  1. Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, Via Vetoio, 67100, L’Aquila, Italy

    Ghulam Mudassir

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  1. Ghulam Mudassir
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Correspondence to Ghulam Mudassir .

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Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Fabiano Dalpiaz

  2. Stockholm University, Kista, Sweden

    Jelena Zdravkovic

  3. The Institute of Digital Innovation and Research, Dublin, Ireland

    Pericles Loucopoulos

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Mudassir, G. (2020). Social-Based Physical Reconstruction Planning in Case of Natural Disaster: A Machine Learning Approach. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_44

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  • DOI: https://doi.org/10.1007/978-3-030-50316-1_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50315-4

  • Online ISBN: 978-3-030-50316-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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