@article{stein2024datadriven,
    type = {article},
    key = {stein2024datadriven},
    title = {Data-driven Prediction of Large Infrastructure Movements Through Persistent Scatterer Time Series Modeling},
    author = {Gideon Stein and Jonas Ziemer and Carolin Wicker and Jannik Jaenichen and Gabriele Demisch and Daniel Kloepper and Katja Last and Joachim Denzler and Christiane Schmullius and Maha Shadaydeh and Clémence Dubois},
    journal = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
    year = {2024},
    pages = {8669-8673},
    doi = {10.1109/IGARSS53475.2024.10642253},
    publisher = {IEEE},
    abstract = {Deformation monitoring is a crucial task for dam operators, particularly given the rise in extreme weather events associated with climate change. Further, quantifying the expected deformations of a dam is a central part of this endeavor. Current methods rely on in situ data (i.e., water level and temperature) to predict the expected deformations of a dam (typically represented by plumb or trigonometric measurements). However, not all dams are equipped with extensive measurement techniques, resulting in infrequent monitoring. Persistent Scatterer Interferometry (PSI) can overcome this limitation, enabling an alternative monitoring scheme for such infrastructures. This study introduces a novel monitoring approach to quantify expected deformations of gravity dams in Germany by integrating the PSI technique with in situ data. Further, it proposes a methodology to find proper statistical representations in a data-driven manner, which extends established statistical approaches. The approach demonstrates plausible deformation patterns as well as accurate predictions for validation data (mean absolute error=1.81 mm), confirming the benefits of the proposed method.},
    note = {},
}