Multi-Sensor Wharf Yard Settlement Forecasting via SSA-PCA Sensor Fusion and Heterogeneous Gradient Boosting

Accurate prediction of wharf yard settlement is difficult because monitoring records combine long-term drift, nonlinear load responses, sensor noise, and redundant exogenous variables. This study presents a three-stage framework that integrates multi-sensor data, separates trend and residual components, and combines heterogeneous learners. An ARIMA model first extracts the linear settlement trend. Singular spectrum analysis then denoises temperature and stress signals, principal component analysis reduces their dimension, and explicit temporal encodings represent daily, weekly, and long-term patterns. A sliding window joins the historical residuals with the exogenous and temporal features. XGBoost, LightGBM, and CatBoost predict the residual correction on complementary feature subsets, with hyperparameters tuned independently for each library, and their out-of-fold predictions are combined by non-negative least squares (NNLS) weights estimated separately for each monitoring station. The trend and residual predictions are finally combined. Tests on 60 days of hourly data from 15 monitoring stations yield an MSE of 1.0098, and an R2 of 0.9492. The method outperforms the LSTM, Prophet-XGBoost, and other evaluated baselines.