Data-Driven Prediction Of Large Infrastructure Movements Through Persistent Scatterer Time Series Modeling

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 es�tablished statistical approaches. The approach demonstrates plausible deformation patterns as well as accurate predictions for validation data (mean absolute error=1.81 mm), confirm�ing the benefits of the proposed method.