Articles | Volume 382
https://doi.org/10.5194/piahs-382-525-2020
https://doi.org/10.5194/piahs-382-525-2020
Pre-conference publication
 | 
22 Apr 2020
Pre-conference publication |  | 22 Apr 2020

Predicting land deformation by integrating InSAR data and cone penetration testing through machine learning techniques

Melika Sajadian, Ana Teixeira, Faraz S. Tehrani, and Mathias Lemmens

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Latest update: 22 May 2024
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Short summary
Cities developed on compressible soils are susceptible to land deformation. Its spatial and temporal monitoring and analysis are necessary for sustainable development of these cities. Techniques such as remote sensing or predictions based on soil characterization can be used to assess such deformations. The objective of this study is to combine these two using machine learning in an attempt to better predict and understand deformations.