Social-based City Reconstruction Planning in case of natural disasters: a Reinforcement Learning Approach

Abstract

Natural disasters always have several effects on human lives. It is always 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). The governments always define plans and policies in accordance with the law and political strategies that should maximize social benefits. The severity of damage and the huge resources needed to bring back life to normality make such reconstruction challenging. This article presents an approach to decision-support system by using deep reinforcement learning technique for the planning of post-disaster city reconstruction by 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 post disaster Rebuilding Plan Provider (pd-RPP) is generic, 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 proposed approach on a district of Sulmona city in Italy.

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