The main objective of the present study was to investigate land subsidence phenomena and the spatiotemporal pattern of groundwater resources in an area located in western Thessaly, Greece, by using remote sensing techniques and data mining methods. Specifically, the nonparametric Mann-Kendall test and the Sen's slope estimator were used to estimate the trend concerning the groundwater table, whereas a set of Synthetic Aperture Radar images, processed with the Persistent Scatterer Interferometry technique, were used investigate the spatial and temporal patterns of ground deformation. Random forest (RF) method was utilized to predict the subsidence deformation rate based on three related variables, namely: thickness of loose deposits, the Sen's slope value of groundwater-level trend, and the Compression Index of the formation covering the area of interest. The outcomes of the study suggest a strong correlation among the thickness of the loose deposits and the deformation rate and also show that a clear trend between the deformation rate and the fluctuation of the groundwater table exists. For the RF model and based on the validation dataset, the r square value was calculated to be 0.7503. In the present study, the potential deformation rate assuming different water pumping scenarios was also estimated. It was observed that with a mean decrease in the Sen's slope value of groundwater-level trend of 20%, there would be a mean decrease of 9.01% in the deformation rate, while with a mean increase in the Sen's slope value of groundwater-level trend of 20%, there would be a mean increase of 12.12% in the deformation rate. The ability of identifying surface deformations allows the local authorities and government agencies to take measures before the evolution of severe subsidence phenomena and to prepare for timely protection of the affected areas.
Keywords: Random forest; Remote sensing techniques; Surface deformation; Water table fluctuation.