基于机器学习的滑坡崩塌地质灾害气象风险预警研究

在划分气象风险等级时，传统地质灾害气象风险预警方法忽略了承灾体脆弱性因素，且气象风险预报等级整体偏高，导致高等级风险区空报率较高。基于此，提出基于机器学习的滑坡、崩塌灾害气象风险预警方法。利用信息量法，分析气象因素影响程度。选取坐标点、降雨量、易发生等级，将其作为机器学习人工神经网络的输入节点，判断是否发生崩塌、滑坡灾害；针对地质灾害区域，根据影响程度计算气象引发因子指数，结合滑坡、崩塌灾害潜势度G和承灾体脆弱性M，确定气象风险预警指数R，划分预警级别，完成滑坡、崩塌灾害气象风险预警。实验结果表明，设计方法有效降低了三级预报和四级预警空报率，提升了预警精细化程度。

Exploring early warning and forecasting of meteorological risk of landslide and rockfall induced by meteorological factors by the approach of machine learning

In the traditional methods of meteorological risk early warning and forecasting, the vulnerability factors of disaster bearing bodies are ignored when classifying the meteorological risk level, and the meteorological risk prediction level is relatively high, which leads to the high air report rate in high-level risk areas. Based on this, a method of early warning and forecasting of meteorological risk of landslide and collapse geological disasters based on machine learning is proposed. By using the information quantity method, the influence degree of meteorological factors is analyzed, and the coordinate point, rainfall and prone level are selected as input nodes of machine learning artificial neural network to judge whether geological disaster occurs; for the area of ground damage, the meteorological cause sub index is calculated according to the influence degree. Combined with the potential degree of geological disaster and vulnerability of disaster bearing body, the meteorological risk warning index is determined, divide the warning and forecast level, and complete the forecast of geological disaster meteorological risk. The experimental results show that the proposed method can effectively reduce the three-level forecast air report rate and the fourth level air alarm rate, and improve the precision of the early warning forecast.