## 1 Introduction
Researchers have also made innovative improvements to the algorithm to improve its accuracy in other areas of prediction. [PERSON]. et al. [12] presented a novel framework called the Stacked Integrated Machine Learning (SEML) method. In this approach, they employed 11 base algorithms as the initial level of modeling, with the LSBoost algorithm acting as the second-level meta-algorithm. This involved utilizing the outputs from the first-level algorithms as inputs. The outcomes of their investigation demonstrate a significant enhancement in both the performance of the base algorithms and the accuracy of wind speed modeling achieved through the SEML method. To investigate the impact of climate change on runoff and suspended sediment load in the Light Basin, [PERSON] et al. [13] introduced a novel approach utilizing a combination of least squares support vector machines and flower pollination algorithms. This approach was then contrasted with an adaptive neuro-fuzzy inference system. The outcomes of the study reveal the superior performance of this new strategy in predicting runoff. The practical significance of its effectiveness in runoff prediction underscores its importance for real-world applications. [PERSON] et al. [14] synergistically employed the least squares support vector machine and the symbiotic organism search algorithm (LSSVM-SOS) to forecast soil compaction parameters. The findings highlight the remarkable predictive accuracy demonstrated by the LSSVM-SOS approach. This algorithm holds substantial practical significance for engineers during the project design phase. For unstable landslides, timely and effective displacement prediction and early warnings are of great significance. Currently, empirical equation prediction methods tend to oversimplify actual conditions and have low reliability. Actual physical models are costly in terms of testing time and materials, and numerical model analysis methods have many variables and rely on parameter settings that do not adequately consider the effects between parameters. Machine learning methods are, therefore, more suitable for slope displacement prediction due to their flexibility and the nature of predicting the future from the past. However, in the existing literature, traditional machine learning methods, such as SVM and time series, have been mainly used to perform static fitting. The individual variables are independent and do not change over time, i.e., they are not dynamic evolutionary models. Dynamic studies are required to truly reflect the dynamic evolutionary nature of the slope. A hybrid algorithm is proposed in this study to predict the degree of slope displacements around rivers. The algorithm optimizes the parameters of the least squares support vector machine (LSSVM) method using the integrated empirical modal decomposition (EEMD) method and the irrational escape strategy sparrow search algorithm (IESSA) method in three steps. First, the displacement data is decomposed using the intrinsic modal function (IMF) method and divided into the trend term and period term. Second, the sparrow search algorithm is optimized by introducing adaptive weight factors and an irrational escape strategy (IESSA) to prevent the function from falling into the local optimum. The third step involves optimizing the LSSVM method, using the IESSA method for machine learning and prediction. The predicted values of slope displacement are obtained by comparing each prediction result. To validate the performance of IESSA, it is subjected to benchmark function validation and compared with other algorithms. The results show that IESSA is the first most efficient algorithm in terms of inspection tests, solution accuracy and convergence speed in function validation.