Abstract: The installation of automatic data acquisition systems, together with the use of machine learning, allow obtaining useful information on the behaviour of dams. In this contribution, an example of application for a machine learning based predictive model is presented. Specifically, the level in a piezometer and its association with the reservoir level is studied for an embankment dam. The results show the model's ability to identify changes in dam response by taking full advantage of the available monitoring data. The flexibility of the algorithm allows different types of variables to be analysed without the need to determine a priori which are the most influential loads or how they affect the target value. The model has been implemented in a software tool that includes additional functionalities, specific for the treatment and exploration of dam monitoring data. It can be applied to different dam types and response variables.

Keywords: Machine Learning, Boosted Regression Trees, Data Analysis, Data Exploration, Embankment Dam.


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