Land Subsidence Modelling Using Data Mining Techniques. The Case Study of Western Thessaly, Greece

  • Paraskevas TsangaratosEmail author
  • Ioanna Ilia
  • Constantinos Loupasakis
Part of the Advances in Natural and Technological Hazards Research book series (NTHR, volume 48)


The main objective of the present study was to investigate land subsidence phenomena and prepare a land subsidence map using spatio-temporal analysis of groundwater resources, remote sensing techniques and data mining methods. The methodology was implemented at the wider plain area extending northwest of Farsala town, Thessaly, Greece, covering an area of approximately 145 Km2. In order to estimate the spatio-temporal trend concerning groundwater level the non-parametric Mann–Kendall test and Sen’s Slope estimator were applied, whereas a set of Synthetic Aperture Radar images, processed with the Persistent Scatterer Interferometry technique, were evaluated in order to estimate the spatial and temporal patterns of ground deformation. In a test site where ground deformation rate values derived by the analysis of SAR images, Support Vector Machines was utilized to predict the subsidence deformation rate based on three variables, namely: thickness of loose deposits, the Sen’s Slope value of groundwater trend and the Compression Index of the formation covering the area of research. Based on the Support Vector Machine model, a land subsidence map was then produced for the entire research area. The outcomes of the study indicated a strong relation between the thickness of the loose deposits and the deformation subsidence rate and a clear trend between the subsidence deformation rate and the groundwater fluctuation. The r square value for the validation dataset within the test site was estimated to be 0.75. The land subsidence map produced by the Support Vector Machine model was validated by field surveys and measurements and showed good predictive performance. In conclusion, the subsidence model proposed in this study allows the accurate identification of surface deformations and can be helpful for the local authorities and government agencies to take measures before the evolution of severe subsidence phenomena and therefore for timely protection of the affected areas.


Land subsidence Remote sensing techniques Water table fluctuation Support vector machine 



The Terrafirma Extension project has funded the SAR imagery processing as well as the geological interpretation presented in this paper. The project is one of the many services supported by the Global Monitoring for Environment and Security (GMES) Service Element Program, promoted and financed by ESA. The project is aimed at providing civil protection agencies, local authorities and disaster management organisms with support in the process of risk assessment and mitigation by using the Persistent Scatterer Interferometry. The authors gratefully acknowledge the German Space Agency (DLR) for having processed the SAR data.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Paraskevas Tsangaratos
    • 1
    Email author
  • Ioanna Ilia
    • 1
  • Constantinos Loupasakis
    • 1
  1. 1.Department of Geological StudiesSchool of Mining and Metallurgical Engineering, National Technical University of AthensZografouGreece

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