The prediction of land subsidence is of significant value for the early warning and prevention of geological disasters. Although numerous land subsidence prediction methods are currently available, two obstacles still exist: (i) spatio-temporal heterogeneity of land subsidence is not well considered, and (ii) the prediction performance of individual models is unsatisfactory when the data do not meet their assumptions. To address these issues, we developed a spatio-temporal heterogeneous ensemble learning method for predicting land subsidence. Firstly, a two-stage hybrid spatio-temporal clustering method was proposed to divide the dataset into internally homogeneous spatio-temporal clusters. Secondly, within each spatio-temporal cluster, an ensemble learning strategy was employed to combine one time series prediction model and three spatio-temporal prediction models to reduce the prediction uncertainty of an individual model. Experiments on a land subsidence dataset from Cangzhou, China, show that the prediction accuracy of the proposed method is significantly higher than that of four individual prediction models.

Understanding the effect mechanisms of various factors on land subsidence may help in the development of scientific measures to control land subsidence. Previous studies mainly focused on exploring local effect mechanisms, such as extracting hotspots and analyzing their spatiotemporal distribution characteristics and identifying the interaction mechanisms of the associated factors. However, the scarcely discussed structural effect mechanisms on a small scale suggests a need to further explore the effects on land subsidence. Therefore, in this paper, an analytical framework was proposed to elaborate the structural effect mechanisms of influencing factors on land subsidence. First, the local effect mechanisms were identified using the geographically and temporally weighted regression (GTWR) model, followed by a spatial clustering analysis and the detection of their aggregation pattern using the spatially constrained multivariate clustering (SCMC) model to show the structural mechanisms. Study datasets included the monitoring results of land subsidence during 2003–2010 and the related socioeconomic factors by using synthetic aperture radar (SAR) images from Beijing. Factors such as population, annual average rainfall, underground water, and static load were identified to measure the changes in land subsidence, and all of these had both negative and positive impacts. Among these, the annual average rainfall had the largest coefficient variation range. These four geographically associated factors revealed various spatiotemporal effects on land subsidence in Beijing, showing land subsidence changes resulting from the urbanization process of Beijing during that period.