Daily Urban Ground Subsidence Occurrence Prediction Using Meteorological Time-Series Data: A Comparative Study in South Korea

Advance prediction and management of ground subsidence are crucial, as its occurrence can lead to human casualties and property damage, particularly in densely populated metropolitan areas. This study developed artificial intelligence (AI)-based models to predict the daily occurrence of urban ground subsidence using meteorological factors. Focusing on selected areas within South Korea, a daily time-series dataset spanning 2010–2015, the primary analysis period selected for record consistency, was constructed using daily precipitation, temperature, and ground subsidence occurrence records. The predictive performance of seasonality-based baselines, conventional machine-learning models (random forest, extreme gradient boosting (XGBoost)) and deep-learning models (long short-term memory (LSTM), LSTM-Transformer (LT)) was evaluated under a strictly chronological, leakage-free protocol with multi-seed repetition and bootstrap confidence intervals. Antecedent meteorological conditions provided predictive skill significantly beyond seasonal climatology; notably, this skill was captured most effectively by a logistic regression on a compact summary of the preceding day’s conditions (macro F1 = 0.608, ROC-AUC = 0.655), which the deep sequence models matched but did not exceed. Analyses of input sequence length showed that short windows outperformed longer ones, and temperature variables emerged as the dominant predictors, indicating that recent antecedent conditions—rather than extended meteorological sequences or model complexity—carry most of the predictive information. This study confirms the feasibility of meteorologically informed daily screening of ground subsidence risk at a prototype level. These findings are expected to facilitate the development of a more robust ground subsidence prediction system through future integration with station-level meteorological inputs and data on subsurface infrastructure and geological conditions.