摘要
为了实现对滑坡灾害进行自动化、智能化、实时化的监测和预报,研究以动态时间规整算法和反向传播神经网络为基础,建立了滑坡灾害预测系统自动化控制模型,对滑坡灾害预测系统进行自动化控制的研究。该模型采用改进反向传播神经网络来实现滑坡灾害预测系统的自动控制,实现了滑坡灾害预测系统自动化控制模型的建立。结果表明,数据集测试中百分数误差远远小于工程误差的10%,决定系数都超过了99%,表明预测值与真实值之间具有很好的相关性,实例验证也说明了该系统的有效性。研究建立的滑坡灾害预测系统自动化控制模型的准确率较高,能够有效地对滑坡灾害进行自动控制,为进一步进行科学有效的研究提供了理论依据和技术支持,具有较好的应用前景。
In order to realize the automatic,intelligent and real-time monitoring and forecasting of landslide disaster,the automatic control model of landslide disaster prediction system was established based on dynamic time regularization algorithm and back propagation neural network,and the automatic control of landslide disaster prediction system was studied.The model uses the improved back propagation neural network to realize the automatic control of the landslide disaster prediction system,and realizes the establishment of the automatic control model of the landslide disaster prediction system.The results show that the percentage error is much less than 10%of the engineering error,and the coefficient of determination is more than 99%,which indicates that there is a good correlation between the predicted value and the real value.The accuracy of the automatic control model of the landslide disaster prediction system is high,which can effectively control the landslide disaster automatically,and provides theoretical basis and technical support for further scientific and effective research,and has a good application prospect.
作者
陈隆
杨歌
陈汉章
CHEN Long;YANG Ge;CHEN Hanzhang(Chn Energy Digital Intelligence Technology Development(Beijing)Co.,Ltd.,Beijing 100011,China;Beijing Longruan Technologies,Beijing 100190,China)
出处
《自动化与仪器仪表》
2024年第7期220-224,共5页
Automation & Instrumentation
基金
矿山地测管理与地质灾害预警信息系统开发与应用国家能源集团科技创新项目
GJNY-20-220。
关键词
动态时间规整
BP神经网络
滑坡灾害预测
自动控制
dynamic time warping
BP neural network
landslide disaster prediction
automatic control
