基于遗传模拟退火算法的滑坡位移预测方法

Landslide displacement prediction based on the Genetic Simulated Annealing algorithm

滑坡是一种常见的地质灾害,通常在复杂的地质条件下演化和发生,给社会和人类的生命财产安全造成了极大的危害。了解滑坡的发展规律,对灾害防治具有重要意义。在现有滑坡累积位移时间序列的基础上,提出了一种基于遗传模拟退火算法的滑坡位移预测方法。采用遗传模拟退火算法-BP神经网络对白水河滑坡预警区Z118观测点进行分析,利用前3个月的累积位移来预测第4个月的累积位移。分别与BP神经网络模型和Elman神经网络模型进行比较,并将遗传模拟退火算法的预测结果与支持向量机的预测结果进行比较。研究结果表明,建立的滑坡位移预测模型能有效地提高预测精度。

The landslide,the evolution of which usually occurs under complex geological conditions,and which brings about great damage to human life and property,is a common geological disaster.Understanding the development of landslides is important for the prevention and control of these disasters.Using field time series data on cumulative landslide displacement,a landslide displacement prediction method based on the Genetic Simulated Annealing algorithm was proposed.The Genetic Simulated Annealing algorithm optimized BP neural network was used to analyze observation point Z118 in the Baishui River landslide warning area.The cumulative displacement data of the first 3 months was applied to predict the accumulated displacement of the 4 month.The results of the BP neural network model and the Elman neural network model were compared.At the same time,the prediction results of the Genetic Simulated Annealing algorithm and the Support Vector Machine model were compared.The results showed that the landslide displacement prediction model established in this article can improve the accuracy of the prediction,and provide a reference for landslide displacement prediction in engineering construction.