Predicting Rainfall-induced Landslide Using Bee Colony Algorithm Based on Support Vector Regression

Abstract

Objective: Natural disasters caused by landslides have done great harm to agricultural production, people's lives, and property. Considering the slope disaster caused by heavy rainfall, it is important to establish an early warning system to monitor rainfall disaster prevention. Huafang University Slope Sustainable Development Research Center (HUSSDRC) has set up a meteorological station equipped with many sensors to provide early warning for landslides in Taiwan. Since the amount of data collected will soon become very large, there is a need to implement strong parallel frameworks containing information from the meteorological station and the displacement of tiltmeters required to predict the landslides caused by rainfall. Apache Spark (AS) is a general framework that contains the parallel process engine for data analytics. In this study, a hybrid method is utilized to predict rainfall-induced landslides. The proposed method combines support vector regression (SVR) with an artificial bee colony (ABC) algorithm on the parallel platform of AS. For the proposed method, the RMSE is 0.562, and it is the best value among these compared approaches. Methods: The SVR together with an ABC algorithm is applied to predict rainfall-induced landslides on AS. The AS can perform parallel data analytics in memory to speed up performance. However, it is hard to set up the best parameters for SVR. Thereafter, the ABC algorithm is utilized to search for the best parameters for SVR. Results: Compared with other methods, the proposed method results provide the smallest root mean square error (RMSE) for predicting rainfall-induced landslides. Conclusion: A hybrid method is proposed to predict rainfall-induced landslides. The proposed hybrid method is based on the parallel platform of AS in which SVR predicts the rainfall-induced landslides, and the ABC algorithm adjusts the best values of parameters for SVR. The comparison of RMSE for the method with existing approaches shows that the method indeed has the best value among compared approaches.