# Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm
## 4 Experiment and Analysis  
Studies reported that the occurrence of landslide hazards has been associated with the geological environment (e.g., rainfall, geological structure and groundwater distribution) [27]. In the experiment, setting the groundwater level of the monitoring point, the rainfall, the water level of the parallel river in the Three Parallel Rivers, and the cumulative displacement of the landslide monitoring point are the antecedents of the landslide occurrence, and the landslide occurs, which is detectable. The landslide monitoring data from 2000 to 2011 in the Three Parallel Rivers area were taken as the training pattern, and the landslide monitoring data from 2012 to 2013 as the test data [28]. For the convenience of calculation, each group of data was standardized and split into averages and low-value areas [29], covering Under_min (monitoring point low water table), Under_max (monitoring point high water table)), Rain_min (small rainfall), Rain_max (large rainfall), River_min (low water level), River_max (high water level), LS1_min (small displacement of the upper bridge monitoring station), LS_max (large displacement of the upper bridge monitoring station), LS2_min (small multi-monitoring station displacement), LS2_max (large multi-monitoring station displacement), LS3_min (small displacement of Jiaodian monitoring station), LS3_max (large displacement of Judian monitoring station), LanslideD_1 (Formation lithology is Gravel soil), LanslideD_2 (Formation lithology is Slate), LanslideD_3 (Formation lithology is Conglomeratic clay), Lanslide1 = 1 (landslide near ShangQiaotou), Lanslide2 = 1 (Landslide near XiangDuo), Lanslide3 = 1 (landslide near JuDian), etc. Given the nature of the Apriori algorithm, to analyze the association rules of landslide monitoring data, the association rules should be first formulated according to the algorithm logic. In this paper, any elements in the former (X) were randomly combined to formulate a series of association rules (see Table 2). The minimum support was set to 0.01 [29], and the confidence degree of each of the mentioned association rules should be calculated by the algorithm. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \begin{table}
\begin{tabular}{c l} \hline \hline
**Rule** & **Information** \\ \hline Rule 1 & **If** Under_max and Rain_max and River_min and LS1-1 and LanslideD_1 **then** Lanslide1 = 1 \\ Rule 2 & **If** Under_max and Rain_max and River_min and LS1-1 and LanslideD_2 **then** Lanslide1 = 1 \\ Rule 3 & **If** Rain_min and River_min and LS1-0 and LanslideD_3 **then** Lanslide1 = 1 \\ Rule 4 & **If** Under_max and Rain_max and River_max and LS2-1 and LanslideD_3 **then** Lanslide2 = 1 \\ Rule 5 & **If** Under_max and Rain_max and River_min and LS2-1 and LanslideD_3 **then** Lanslide2 = 1 \\ Rule 6 & **If** Under_max and Rain_max and River_min and LS3-1 and LanslideD_2 **then** Lanslide3 = 1 \\ Rule 7 & **If** Rain_max and River_min and LS1-1 and Lssl3-1 and LanslideD_2 **then** Lanslide3 = 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of some association rules (minsupp = 1%). On the whole, the association rule exhibiting a confidence above 0.7 is considered a strong association rule [17; 29; 30]. Association rules with low confidence retain rules with a confidence level above 0.7 are deleted, and a series of useful information is calculated, laying a basis for landslide warning. The value of confidence in association rules is critical to model prediction. The higher the confidence, the more accurate the landslide prediction will be [29]. Thus, under the same experimental conditions, the confidence of IAprioriMR algorithm and FP-Growth algorithm were compared, and the IAprioriMR algorithm is was analyzed in mid-long-term early warning of landslides. According to the experimental results, under the same conditions, the IAprioriMR algorithm (see Table 3) of the same rule exhibits a higher confidence than FP-Growth algorithm (see Table 4), so IAprioriMR can achieve more accurate landslide prediction.