基于峰值地加速度多传感器系统的地震减灾研究

C. Setianingsih, M. A. Murti, Alifi Adham Wicaksono, R. E. Saputra, Dimas Budi Pangestu
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引用次数: 0

摘要

印度尼西亚是一个处于构造板块交汇点的国家,因此很可能会经历地震的自然现象。地震是人类无法预测的自然事件,会造成致命的影响,比如死亡人数和造成大量物质损失的建筑物破坏。这个系统的建立是为了减少自然灾害的二次影响。随着以报警和自动切断系统形式存在的地震减灾系统的存在,希望该系统可以最大限度地减少破坏和生命损失,提高地震警觉性。随着技术的发展,本研究的主要目的之一是使人们意识到在报警后立即撤离,并切断一些关键的流量,如电、气等。该系统利用了多传感器技术、物联网和人工智能。该系统实现了k -最近邻(KNN)和逻辑回归两种算法。在具有3个有源传感器的KNN中,结果精度为94%。而使用10个有源传感器的logistic回归,结果准确率为83%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Earthquake Disaster Mitigation Based on Peak Ground Acceleration with Multi-Sensor System
Indonesia is a country at the meeting point of tectonic plates, so it will likely experience the natural phenomenon of earthquakes. Earthquakes are natural events that humans cannot predict and cause fatal impacts, such as the number of fatalities and damage to buildings that cause a lot of material losses. This system was created to reduce the secondary effect of natural disasters. With the existence of an earthquake mitigation system in the form of an alarm warning and auto cut-off system, it is hoped that this system can minimize damage, loss of life and increase the level of earthquake alertness. Along with the development of technology, one of the main objectives of this research is to make people aware of evacuating immediately after the alarm and cut off some crucial flows such as electricity, gas, and others. This system utilizes multi-sensor technology, the Internet of Things, and Artificial Intelligence. This system implements 2 algorithms, such as K-Nearest Neighbor (KNN) and Logistic Regression. In KNN with 3 active sensors, the resulting accuracy is 94%. While Logistics Regression with 10 active sensors, the resulting accuracy is 83%.
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