移动应用中基于蚁群优化的水位监测与疏散指南

W. Kimpan, Sirawich Kasetvetin, C. Kimpan
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引用次数: 0

摘要

泰国发生的最严重的自然灾害是风暴和洪水问题。居住在水源地附近的人们没有收到附近水溢出的警告,因此他们无法及时撤离或获得帮助。因此,损失财产或生命的风险总是很高。为了减轻损失,本文提出了基于物联网设备的Android应用的水位监测和基于真实蚁群的蚁群优化疏散指南。物联网设备用于监测社区水位,供居住在水源附近或洪水高风险地区附近的用户使用。静压液位传感器放置在社区附近的水盆中,以测量水的高度,也可以通过移动应用程序实时观察。当水的高度达到应用程序中设置的临界值时,它会向用户发送通知。此外,Line bot还用于让用户了解水位上升的潜在风险。当达到临界水平时,用户需要疏散到附近安全的地方。该应用程序将引导用户按照方向到达最安全的目的地。如果一个地方已经疏散了很多人,并且达到了最大数量限制,应用程序将使用蚁群优化算法进行决策,将推荐方向更改为附近的其他地方。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Water Level Monitoring and Evacuation Guideline Using Ant Colony Optimization on Mobile Application
The most natural disasters that have happened in Thailand are storm and flood problems. The people who live near water sources have no warning about the overflowing of water nearby, so they cannot evacuate or get help in time. Thus, there is always a high risk of losing properties or lives. In order to alleviate the losses, this paper proposes water level monitoring on Android application from Internet of Things devices and the guideline for evacuation by applying Ant Colony Optimization which is inspired by the real ant colony. Internet of Things devices are used to monitor the water levels in community for the user who lives near the water sources or near the places which have high risk of flooding. The Hydrostatic level sensors are placed in the water basin near the community to measure the height of the water which can also be observed in real time from mobile application. When the height of the water reaches the critical value that was set in the application, it sends notifications to the user. Moreover, Line bot is used to let the user knows the potential risks from rising water levels. At the critical level, the user needs to evacuate to a safe place located nearby. The application will guide the user to follow the direction to the most safety destination. In case of many people are already evacuated in one place and it reached the maximum amount of limitation, the application will change the recommendation direction to other places nearby using Ant Colony Optimization algorithm for making decisions.
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