## 1 Introduction  
The importance of monitoring land subsidence in construction projects cannot be overlooked, as neglecting this aspect could lead to severe consequences [1; 2]. Firstly, failing to monitor land subsidence can pose safety hazards at construction sites, such as collapses or landslides, endangering the lives of workers and damaging equipment. Secondly, after the completion of the project, structural issues such as cracks or wall deformations may arise, increasing maintenance costs. Even more seriously, the project party might face legal litigation, bear compensation, and suffer damage to their reputation as a result. Therefore, monitoring land subsidence has always been a crucial step in ensuring construction safety and project quality. Currently, numerous scholars and researchers have delved deeply into the prediction of land subsidence, and the research approaches can be broadly categorized into two primary types. The first is prediction methods based on independent models. For instance, [PERSON] et al. [3] analyzed the excessive surface subsidence caused by pit excavation using various machine learning algorithms. [PERSON] et al. [4] employed three neural network methods to determine the model best suited for predicting maximum ground settlement during tunnel construction. [PERSON] et al. [5] conducted research on the maximum surface subsidence of urban tunnels using seven intelligent methods: long short-term memory (LSTM), deep neural network (DNN), K-nearest neighbor (KNN), Gaussian process regression (GPR), support vector regression (SVR), decision tree (DT), and linear regression (LR). [PERSON] et al. [6], addressing the issue of ground subsidence during pit construction, proposed a daily land subsidence prediction method based on artificial neural networks. They demonstrated that time-related influencing factors and previous subsidence monitoring data play a crucial role in predicting daily ground subsidence. [PERSON] et al. [7] integrated four different machine learning methods, including support vector machine (SVM), random forest (RF), back propagation neural network (BPNN), and deep neural network (DNN), to estimate the maximum surface subsidence caused by tunnel excavation. Although independent models had achieved some results in predicting land subsidence, their prediction accuracy still needed improvement. Therefore, many researchers began to explore another prediction strategy, namely, using combined models for prediction. For example, [PERSON] et al. [8] proposed a prediction method for the maximum surface subsidence during shield tunneling, using the BPNN and random forest algorithm (RF) machine learning algorithms, with the optimal hyperparameters determined through the particle swarm optimization (PSO). [PERSON] et al. [9] introduced a novel hybrid model of artificial neural networks optimized using the imperialist competitive algorithm (ICA-ANN) and precisely predicted the maximum surface subsidence caused by surface structural damage and environmental issues. [PERSON] et al. [10] established a hybrid model by combining three types of meta-heuristic algorithms: ant lion optimizer (ALO), multi-verse optimizer (MVO), and grasshopper optimization algorithm (GOA) to accurately predict the land subsidence caused by underground tunnel construction. [PERSON] et al. [11] used the LSTM to predict land subsidence at typical locations in different subsidence development areas, providing reliable technical support for land subsidence prevention and control. Existing research clearly showed that ensemble models outperform single models in prediction accuracy, making them particularly favored in land subsidence forecasting. Currently, forecasting efforts are mainly based on existing data related to land subsidence to predict future trends. Given that land subsidence data are often closely associated with time series, selecting an appropriate model is crucial for enhancing forecasting accuracy.