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利用实时传感器和GPS算法加强森林火灾疏散规划。

Enhanced forest fire evacuation planning using real-time sensor and GPS algorithm.

作者信息

Sharma Vishal, Nagpal Deepali, Monga Suhasini, Almogren Ahmad, Srivastava Durgesh, Altameem Ayman, Choi Jaeyoung

机构信息

Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.

Department of Computer Science, College of Computer and Information Sciences, King Saud University, 11633, Riyadh, Saudi Arabia.

出版信息

Sci Rep. 2024 Aug 29;14(1):20091. doi: 10.1038/s41598-024-71052-8.

DOI:10.1038/s41598-024-71052-8
PMID:39209969
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11362607/
Abstract

Forest fires are the source of countless fatalities and extreme economic repercussions. The safe evacuation of residents of an area affected by forest fires is the highest priority of local authorities, and finding the most optimal course of action has been a primary research focus for years. Previous studies over several decades have attempted to find an optimal solution using the applications of bug navigation systems, road network reconfiguration, graph traversals, swarm optimization, etc. The author, with the motivation to prevent human casualties at the time of such calamity, presents a novel study which solves the problem in nearly linear time computation, surpassing the performance standards of previous research, and accommodates the unpredictability of the spread of forest fires. This includes a proposal of an algorithm which builds upon the application of Spielman and Teng's Electrical Circuit Approach to solve for maximum flow in a network and implements this with real-time sensor and Global Positioning System input.

摘要

森林火灾会造成无数人员伤亡和极其严重的经济影响。森林火灾受灾地区居民的安全疏散是地方当局的首要任务，多年来，寻找最优行动方案一直是主要研究重点。过去几十年的研究试图通过应用昆虫导航系统、道路网络重新配置、图遍历、群体优化等方法找到最优解。作者出于在这类灾难发生时防止人员伤亡的动机，提出了一项新颖的研究，该研究在近线性时间计算中解决了问题，超越了先前研究的性能标准，并适应了森林火灾蔓延的不可预测性。这包括提出一种算法，该算法基于斯皮尔曼和滕的电路方法来求解网络中的最大流，并通过实时传感器和全球定位系统输入来实现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e3/11362607/4dacd3c65433/41598_2024_71052_Fig9_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e3/11362607/d13f51c8ea30/41598_2024_71052_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e3/11362607/6ef7feb478fc/41598_2024_71052_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e3/11362607/4dacd3c65433/41598_2024_71052_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e3/11362607/db63d627a4bc/41598_2024_71052_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e3/11362607/e95c254b2c42/41598_2024_71052_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e3/11362607/e517b6e5a855/41598_2024_71052_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e3/11362607/87dd9b33ca29/41598_2024_71052_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e3/11362607/1aeb900bc151/41598_2024_71052_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e3/11362607/95cba9c8b0e4/41598_2024_71052_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e3/11362607/d13f51c8ea30/41598_2024_71052_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e3/11362607/6ef7feb478fc/41598_2024_71052_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e3/11362607/4dacd3c65433/41598_2024_71052_Fig9_HTML.jpg

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本文引用的文献

1
Intelligent Evacuation Route Planning Algorithm Based on Maximum Flow.基于最大流的智能疏散路径规划算法。
Int J Environ Res Public Health. 2022 Jun 27;19(13):7865. doi: 10.3390/ijerph19137865.
2
Path planning and smoothing of mobile robot based on improved artificial fish swarm algorithm.基于改进人工鱼群算法的移动机器人路径规划与平滑。
Sci Rep. 2022 Jan 13;12(1):659. doi: 10.1038/s41598-021-04506-y.
3
Forest fire detection system using wireless sensor networks and machine learning.基于无线传感器网络和机器学习的森林火灾检测系统。
Sci Rep. 2022 Jan 7;12(1):46. doi: 10.1038/s41598-021-03882-9.