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一个基于整体学习的实验框架,用于在物联网云环境中的智能滑坡检测,监测,预测和警告
Aman Sharma1, Rajni Mohana2,3, Ashima Kukkar4
1CS/IT Department, Jaypee University of Information and Technology, Solan, India.
Environmental science and pollution research international
|November 16, 2023
概括
本研究介绍了一种基于物联网 (IoT) 的低成本系统,用于发现和警告山体滑坡. 该系统使用传感器和集体学习来预测山体滑坡,为当局提供实时警报.
科学领域:
- 地质科学和环境监测
- 工程和技术工程和技术.
- 灾害管理 灾害管理
背景情况:
- 滑坡是经常发生的,具有破坏性的自然灾害,特别是在丘陵地区的季风季节.
- 现有的山体滑坡监测工具成本高昂,缺乏有效的预警能力.
- 迫切需要高效,低成本的山体滑坡预测和预警系统.
研究的目的:
- 开发和评估一个创新的,低成本的物联网 (IoT) 系统,用于山体滑坡预警,监测和预测.
- 实施一个用于实时环境监测和灾难预警的智能系统.
- 整合一个集体学习模型,用于准确地预测山体滑坡风险.
主要方法:
- 利用传感器和硬件实时监测易滑坡地区.
- 开发了一个物联网系统用于数据传输和基于值的警报生成.
- 应用集体学习模型对静态和动态数据进行滑坡风险预测.
主要成果:
- 原型系统在检测山体滑坡方面表现出80-85%的准确率.
- 实时数据更新发生在2.5秒内,图形显示在5秒内可用.
- 集体学习模型实现了高性能指标:98.67%的回忆,96.56%的准确性,97.35%的F1值和96.07%的精度.
- 向有关当局发送了自动SMS警报.
结论:
- 拟议的基于物联网的系统为山体滑坡监测和预警提供了有效和低成本的解决方案.
- 集体学习的整合显著提高了山体滑坡预测的准确性.
- 该系统为当局提供及时的警报,有助于应对灾害和减轻灾害的努力.
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