Genetic mechanism analysis and development trend prediction of a rock landslide in Jinhua

A multi-analysis has been conducted to explore the genetic mechanism of a rock landslide in Jinhua City, indicating that the landslide was induced by groundwater movement with the inherent internal factors. An improved Grey Model GM(1,1) using Dynamic Metabolism and Markov Model is proposed to evaluate the landslide development trend and increase the low prediction accuracy of original GM(1,1) caused by staled information and volatility data. The predicted value of Dynamic GM(1,1) is closer to the latest variation trend owing to the dynamic update of data realized by Metabolic Theory. The prediction accuracy is also improved by utilizing Markov Model to correct the predicted value of Dynamic Metabolism GM(1,1). The prediction result shows that the average relative error of prediction of Markov Dynamic Metabolism GM(1,1) is reduced by 70% in the deformation prediction of landslides, compared with the traditional Grey Model (1,1). The Markov Dynamic Metabolism GM (1,1) possesses a far superior prediction accuracy to the traditional GM (1,1) in the matter of the landslide monitoring data with large fluctuation, thus it has practical reference value. Considering that the development trend of the landslides is unstable according to the monitoring data and prediction results in Jinhua City, appropriate control measures should be taken.