Abstract: In this paper, the subtraction-average-based optimizer (SABO) is used to optimize the long short-term memory (LSTM) model, and the SABO-LSTM-based slope displacement monitoring model of water-division project is established, thus achieving the accurate prediction of slope displacement law. The displacement and displacement rate of each measuring point of slope are taken as evaluation indexes. According to the data characteristics of each index, the confidence interval method is used to classify the risk state, and the basic probability assignment (BPA) of each index is calculated according to the interval number distance. Then, the basic probability of each index is fused with the Dempster-Shafer (D-S) evidence theory, and the risk state of slope displacement is evaluated by using the fusion results. Combined with the prediction results of the monitoring model, the pre-evaluation of the risk state of slope displacement is achieved. The research results can provide technical reference for slope displacement prediction and risk state assessment.
Keywords: water diversion project slope; subtraction-average-based optimizer-long short-term memory (SABOLSTM) model; confidence interval method; Dempster-Shafer (D-S) evidence theory; risk state
合肥工业大学学报自科版