@article{stein2025dam,
    type = {article},
    key = {stein2025dam},
    author = {Jonas Ziemer and Gideon Stein and Carolin Wicker and Jannik Jänichen and Daniel Klöpper Katja and Last and Joachim Denzler and Christiane Schmullius and Maha Shadaydeh and Clémence Dubois},
    title = {Enhancing the Prediction of Dam Deformations: A Novel Data-Driven Approach},
    journal = {Remote Sensing},
    volume = {17},
    year = {2025},
    number = {6},
    article-number = {1026},
    url = {https://www.mdpi.com/2072-4292/17/6/1026},
    issn = {2072-4292},
    abstract = {Deformation monitoring is a critical task for dam operators to guarantee safe operation. Given an increasing number of extreme weather events caused by climate change, the precise prediction of dam deformations has become increasingly important. Traditionally, multiple linear regression models have been employed, utilizing in situ data from pendulum systems or trigonometric measurements. These methods sometimes suffer from sparse data, which typically represent deformations only at specific points on the dam, if such data are available at all. Technical advances in multi-temporal synthetic aperture radar interferometry (MT-InSAR), particularly Persistent Scatterer Interferometry (PSI), address these limitations by enabling monitoring in high spatial and temporal resolution, capturing dam deformations with millimeter precision, and providing extensive spatial coverage. This study advances traditional methods of dam monitoring by employing data-driven techniques and integrating Sentinel-1 C-band Persistent Scatterer (PS) time series alongside in situ data. Through a comprehensive evaluation of advanced data-driven approaches, we demonstrated considerable improvements in predicting dam deformations and evaluating their drivers. The analysis provided evidence for the following insights: First, the accuracy of current modeling approaches can be greatly improved by utilizing advanced feature engineering and data-driven model selection. The prediction performance of the pendulum data was improved by utilizing data-driven algorithms, reducing the mean absolute error from 0.51 mm in the baseline model (R2 = 0.92) to as low as 0.05 mm using the full model search space (R2 = 0.99). Although the model accuracy for the PS datasets (MAEmax: 0.81 mm) was about one order of magnitude lower than that for pendulum data, the mean absolute errors could be reduced by up to 0.25 mm. Second, by incorporating freely available PS time series into deformation prediction, dams can be monitored in higher spatial resolution, making PSI a valuable tool for dam operators. This requires adequate dataset filtering to eliminate noisy PS points. Third, extended representations of water level and temperature, including interaction effects, can improve model accuracy and reduce prediction errors. With these insights, we recommend incorporating the proposed methodology into the monitoring program of gravity dams to enhance the accuracy in predicting their expected deformations.},
    doi = {10.3390/rs17061026},
}