Rescue path planning for urban flood: A deep reinforcement learning-based approach.

IF 3 3区 医学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Risk Analysis Pub Date : 2024-08-11 DOI:10.1111/risa.17599
Xiao-Yan Li, Xia Wang
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引用次数: 0

Abstract

Urban flooding is among the costliest natural disasters worldwide. Timely and effective rescue path planning is crucial for minimizing loss of life and property. However, current research on path planning often fails to adequately consider the need to assess area risk uncertainties and bypass complex obstacles in flood rescue scenarios, presenting significant challenges for developing optimal rescue paths. This study proposes a deep reinforcement learning (RL) algorithm incorporating four main mechanisms to address these issues. Dual-priority experience replays and backtrack punishment mechanisms enhance the precise estimation of area risks. Concurrently, random noisy networks and dynamic exploration techniques encourage the agent to explore unknown areas in the environment, thereby improving sampling and optimizing strategies for bypassing complex obstacles. The study constructed multiple grid simulation scenarios based on real-world rescue operations in major urban flood disasters. These scenarios included uncertain risk values for all passable areas and an increased presence of complex elements, such as narrow passages, C-shaped barriers, and jagged paths, significantly raising the challenge of path planning. The comparative analysis demonstrated that only the proposed algorithm could bypass all obstacles and plan the optimal rescue path across nine scenarios. This research advances the theoretical progress for urban flood rescue path planning by extending the scale of scenarios to unprecedented levels. It also develops RL mechanisms adaptable to various extremely complex obstacles in path planning. Additionally, it provides methodological insights into artificial intelligence to enhance real-world risk management.

城市洪水救援路径规划:基于深度强化学习的方法
城市洪水是全世界损失最惨重的自然灾害之一。及时有效的救援路径规划对于最大限度地减少生命和财产损失至关重要。然而,目前有关路径规划的研究往往未能充分考虑评估区域风险不确定性和绕过洪水救援场景中复杂障碍物的需要,这给制定最佳救援路径带来了巨大挑战。本研究提出了一种深度强化学习(RL)算法,其中包含四种主要机制来解决这些问题。双优先经验重播和回溯惩罚机制增强了对区域风险的精确估计。同时,随机噪声网络和动态探索技术鼓励机器人探索环境中的未知区域,从而改进采样并优化绕过复杂障碍物的策略。该研究以现实世界中重大城市洪水灾害的救援行动为基础,构建了多个网格模拟场景。这些场景包括所有可通过区域的不确定风险值,以及更多复杂元素的存在,如狭窄通道、C 形障碍物和锯齿状路径,大大提高了路径规划的挑战性。对比分析表明,只有所提出的算法能够绕过所有障碍物,并在九种情况下规划出最佳救援路径。这项研究通过将场景规模扩展到前所未有的水平,推进了城市洪水救援路径规划的理论进展。它还开发了适应路径规划中各种极其复杂障碍的 RL 机制。此外,它还提供了人工智能的方法论见解,以加强现实世界的风险管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Risk Analysis
Risk Analysis 数学-数学跨学科应用
CiteScore
7.50
自引率
10.50%
发文量
183
审稿时长
4.2 months
期刊介绍: Published on behalf of the Society for Risk Analysis, Risk Analysis is ranked among the top 10 journals in the ISI Journal Citation Reports under the social sciences, mathematical methods category, and provides a focal point for new developments in the field of risk analysis. This international peer-reviewed journal is committed to publishing critical empirical research and commentaries dealing with risk issues. The topics covered include: • Human health and safety risks • Microbial risks • Engineering • Mathematical modeling • Risk characterization • Risk communication • Risk management and decision-making • Risk perception, acceptability, and ethics • Laws and regulatory policy • Ecological risks.
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