# Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm
## 1 Introduction  
Geological disasters pose a serious threat to the safety of human life and private property, and landslides are one of the commonest geological disasters. The occurrence of landslides not only seriously threatens the safety of human life and private property, but also severely damages the environment and ecology [1]. One of the feasible ways to comprehensively control landslide hazards is to predict landslide hazards [2].  
In accordance with system theory and nonlinear science, many novel techniques have been employed to study the stability of landslides, a range of comprehensive prediction models have been summarized, and landslide prediction criteria have been proposed, including decision tree [3; 4], generalized FLaIR model (GFM) [5], artificial neural network [6], object-oriented methods [7] and support vector machine (SVM) [8]. The landslides of different causes were systematically summarized,and these landslides of different types were comprehensively analyzed to build prediction models for different landslides [9]. However, these prediction models are not applicable to the mid-long-term application of landslide hazards [10]. Furthermore, landslide occurrence is difficult to predict accurately, and the mid-long-term early warning of landslides is hard to achieve [11].  
Recently, the big data technology has been increasingly employed for landslide disaster early warning [12; 13]. Through the analysis of historical landslide data, the use of big data technology becomes a hotspot for prompt and feasible landslide mid-long-term early warning. The distributed landslide prediction model does not enhance the efficiency of the algorithm. In the study on mid-long-term landslide warning, the operational efficiency of the prediction model is vital to the timeliness of landslide prevention and control [14].  
Association rule mining can be used to determine the levels of association among various causes [15]. Association rule mining has been successfully applied to uncover such cause-and-effect relationships in a variety of fields, including causes of occupational accidents [16] and prediction landslide hazards [17]. When the Apriori algorithm runs based on the MapReduce framework, the Apriori algorithm will generate numerous frequent item-sets, significantly affecting the efficiency [18]. To solve this problem, the Apriori algorithm runs based on the MapReduce framework [13; 18], whereas it does not optimize the algorithm for landslide data correlation analysis based on the MapReduce framework. In the processing of large-scale landslide data, the algorithm has low efficiency since it should generate many frequent items, which adversely affects the timely prevention and control of landslides [18; 19].  
To enhance the computational efficiency of the prediction model, the Apriori algorithm was optimized based on the MapReduce framework, and the IAprioriMR algorithm is proposed in this paper. The Three Parallel Rivers area with frequent geological disasters was taken as the study area. The relevant data of landslides in the study area were collected. The feasibility of the algorithm was verified using the IAprioriMR for the mid-long-term early warning of landslide.