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

TL;DR

The paper addresses post-disaster reconstruction under tight budgets and timelines by formulating REPAIR, a generic decision-support system that uses Double Deep Q-Network (DDQN) planning to maximize social benefits. It jointly models physical dependencies, political priorities, and social benefits within a graph-based infrastructure representation and evaluates multiple reconstruction plans in a real case study of L'Aquila after the 2009 earthquake. The contributions include a formal optimization objective with a neighborhood social-benefit term, a data-processing pipeline from shapefiles to G and G', a DDQN-based planner with a customized reward function, and an empirical comparison showing DDQN outperforms baselines. The work demonstrates practical implications for municipal decision-makers by providing ready-to-run plans and insights into prioritization under constraints, while outlining directions to improve scalability and damage-assessment accuracy.

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.

Figures (10)

• Figure 1: Physical Dependencies Modeling
• Figure 2: Social Benefits of Plan $P_1$ and Plan $P_2$
• Figure 3: Proposed Methodology
• Figure 4: L'Aquila city map
• Figure 5: Damage buildings and roads