Logistic regression optimized SVR-LSTM landslide displacement prediction method and system

The invention discloses a logistic regression optimized SVR-LSTM landslide displacement prediction method and system. The method comprises the following steps: collecting landslide displacement data of each monitoring point, and decomposing the data into trend term data and periodic term data by adopting an average movement method; constructing a data set according to the landslide displacement data, preliminarily screening candidate input factors, and performing Pearson correlation coefficient analysis on the candidate input factors and the periodic term data to obtain model input factors; utilizing a support vector regression algorithm and an LSTM (Long Short Term Memory) algorithm to predict the trend term and the period term respectively, and acquiring total displacement prediction results corresponding to the two algorithms respectively; taking the model input factor and the total displacement prediction result as alternative factors of an LR classification algorithm, calculating weights of a support vec