## 1 Introduction  
More and more buildings, roads and railways are built on soft soil, and their construction schemes and use functions require more accurate determination of their settlement in the construction period and operation period [1]. Now there are effective stabilization technologies to reduce settlement, such as the realization of pile foundations [2, 3]. However, the settlement prediction of soft soil is still an important topic in civil engineering research to effectively prevent disasters [4, 5, 6]. Common soft soil settlement prediction methods include curve-fitting method and system theory method, among which curve-fitting method includes hyperbola method, exponential curve method, three-point method, Asaoka method, settlement rate method, Xinye method, etc. [7], and the system theory method includes grey theory method, neural network method, Support Vector Machines (SVM), and its improvement method [8, 9, 10, 11]. Least Squares Support Vector Machines (LSSVM) is one of the improvement methods of SVM, which changes the problem of solving quadratic programming in the optimization problem of SVM into the problem of solving linear equations. Similar to SVM methods, they are based on statistical learning theory and adopt the principle of minimum structural risk to maximize their generalization ability and better solve practical problems such as nonlinearity, high dimension and local minimum. The difference is that LSSVM method simplifies the model solving difficulty and improves the model calculation speed compared with SVM method [12]. However, the current LSSVM method still has the deficiency of less guidance in the search process or the optimized parameters are not the global optimal solution when solving the model parameters. Therefore, it is necessary to conduct in-depth research on the LSSVM method to be more suitable for the prediction of soft ground settlement. In terms of settlement prediction, the LSSVM method is the same as the SVM method, and the selection of hyperparameters has a great influence on the solution accuracy of the prediction problem. Therefore, the choice of what kind of effective method to establish the hyperparameters of the model becomes a key problem of SVM model or LSSVM model. Research on this problem can be summarized into two categories: one is intelligent method and optimization method of parameter selection. For example, [PERSON] et al. combine non-dominated sorting genetic algorithm (NSGA II) with a learning algorithm (neural network) to establish a prediction model based on SPT data based on Pareto optimal frontier [13]. [PERSON] et al. introduced MAE, MAPE, and MSE as the criteria to evaluate the prediction accuracy of SP-LSSVM and MP-LSSVM, and then optimized LSSVM hyperparameters [14]. Similarly, [PERSON] et al. proposed MAE and RMSE optimization model parameters, and explained the correspondence between WPT-LSSVM model prediction and actual observation [15]. [PERSON] et al. used 18 statistical parameters, such as RMSE and T-STAT to optimize LSSVM model parameters, and compared the reliability of LSSVM, GMDH and GPR models [16]. Another method is to optimize parameters by using the physical characteristics of samples in the model, including the output error of samples, the algebraic distance of samples, the number of key samples, etc. For example, [PERSON] et al. selected geotechnical parameters related to the geometric shape of shallow foundation as the input values of training samples, determined regularization parameters by analyzing the correlation coefficient of output values, and proved that this method has good usability through testing samples [17]. [PERSON] et al. used physical characteristics such as rainfall, minimum temperature, and maximum temperature at different elevations as input values of training samples, selected parameters related to output values, and used relevant physical quantities at another elevation as test samples to compare the overall performance of LSSVM model and SDSM model [18]. [PERSON] et al.