REPAIR Approach for Social-based City Reconstruction Planning in case of natural disasters

TL;DR

This paper presents REPAIR, a deep reinforcement learning-based decision support system for optimizing city reconstruction plans after natural disasters, demonstrated through a case study of L'Aquila's 2009 earthquake recovery.

Contribution

It extends previous work by integrating additional deep learning models and a random agent, providing a generic, adaptable framework for post-disaster city reconstruction planning.

Findings

REPAIR effectively generates multiple reconstruction plans.

The system maximizes social benefits considering resource constraints.

Application to L'Aquila case demonstrates practical utility.

Abstract

Natural disasters always have several effects on human lives. It is challenging for governments to tackle these incidents and to rebuild the economic, social and physical infrastructures and facilities with the available resources (mainly budget and time). Governments always define plans and policies according to the law and political strategies that should maximise social benefits. The severity of damage and the vast resources needed to bring life back to normality make such reconstruction a challenge. This article is the extension of our previously published work by conducting comprehensive comparative analysis by integrating additional deep learning models plus random agent which is used as a baseline. Our prior research introduced a decision support system by using the Deep Reinforcement Learning technique for the planning of post-disaster city reconstruction, maximizing the social benefit of the reconstruction process, considering available resources, meeting the needs of the broad community stakeholders (like citizens' social benefits and politicians' priorities) and keeping in consideration city's structural constraints (like dependencies among roads and buildings). The proposed approach, named post disaster REbuilding plAn ProvIdeR (REPAIR) is generic. It can determine a set of alternative plans for local administrators who select the ideal one to implement, and it can be applied to areas of any extension. We show the application of REPAIR in a real use case, i.e., to the L'Aquila reconstruction process, damaged in 2009 by a major earthquake.