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首页> 外文会议>IEEE Annual Computers, Software, and Applications Conference >Social-based City Reconstruction Planning in case of natural disasters: a Reinforcement Learning Approach
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Social-based City Reconstruction Planning in case of natural disasters: a Reinforcement Learning Approach

机译：自然灾害情况下基于社会的城市重建规划：一种强化学习方法

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摘要

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.
机译：自然灾害总是会对人类生活产生多种影响。对于政府来说，利用现有资源（主要是预算和时间）处理这些事件，重建经济、社会和有形基础设施和设施，一直是一个挑战。政府总是根据法律和政治战略制定计划和政策，以实现社会效益最大化。破坏的严重性和恢复正常生活所需的巨大资源使重建工作充满挑战。本文提出了一种基于深度强化学习技术的城市灾后重建规划决策支持系统，满足广大社区利益相关者的需求（如公民的社会福利和政客的优先事项），并考虑城市的结构约束（如道路和建筑之间的依赖性）。建议的灾后重建计划提供者（pd RPP）方法是通用的，可以为当地管理员确定一组备选计划，供他们选择理想的计划来实施，并且可以应用于任何扩展的区域。我们在意大利苏尔莫纳市的一个地区展示了所提出的方法。

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