基于机器学习的监测和预测滑坡现象的方法
Van-Tinh Nguyen1, Quang-Anh Nguyen1, Ngoc-Kien Nguyen1
1School of Mechanical Engineering, Hanoi University of Science and Technology, Vietnam.
MethodsX
|July 5, 2024
概括
这项研究引入了一种使用机器学习和物联网 (IoT) 进行山体滑坡预测的新方法. 该系统分析数据以预测山体滑坡事件,从而提前警告并减少潜在的损失.
科学领域:
- 地质与地球科学 地质与地球科学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 滑坡是由于重力超过斜坡材料强度而造成的自然灾害.
- 切削强度取决于摩擦力和凝聚力,对于斜坡稳定性至关重要.
- 早期发现和预防山体滑坡对于减轻对生命和经济的损害至关重要.
研究的目的:
- 开发一种方便的方法来理解山体滑坡现象.
- 预测山体滑坡事件并减少潜在的损失.
- 实施有效的山体滑坡预警系统.
主要方法:
- 收集和分析数据,以评估山体滑坡的可能性.
- 机器学习模型用于滑坡预测的应用.
- 利用物联网 (IoT) 系统进行实时警报.
主要成果:
- 拟议的方法简化了掌握滑坡现象信息.
- 机器学习模型有效地预测了滑坡事件.
- 物联网系统成功地通过电子邮件和移动设备管理和发送及时警告.
结论:
- 开发的方法提供了一个用户友好的方法来进行山体滑坡数据分析.
- 机器学习和物联网集成提高了山体滑坡预测的准确性和响应.
- 这种综合系统对于有效的山体滑坡风险管理和防灾准备至关重要.
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