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

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.