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• W2904533134 abstract "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." @default.
• W2904533134 created "2018-12-22" @default.
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• W2904533134 date "2018-12-13" @default.
• W2904533134 modified "2023-09-25" @default.
• W2904533134 title "Land Subsidence Modelling Using Data Mining Techniques. The Case Study of Western Thessaly, Greece" @default.
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• W2904533134 doi "https://doi.org/10.1007/978-3-319-73383-8_4" @default.
• W2904533134 hasPublicationYear "2018" @default.
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