Predictive models are fundamental tools for providing dam behavior interpretation and analysis, and they are essential tools used to retrieve conclusions about the structural safety of these dams. The data for these predictive models are gathered through sensors from the monitoring system of the dam. Even though predictive models are powerful tools for analysis and prediction, other machine learning and statistical models, such as artificial neural networks, have been developed over the years. Due to the importance of the redundancy of models to perform dam safety analyses, the focus is to improve the existing methods by comparing them with each other. This work focuses on developing a methodology that compares different predictive models, like the Multiple Linear Regression Model, the Ridge Regression Model, the Principal Component Regression Model, and Neural Networks. This methodology is applied to a case study to find which methods provide the highest accuracy when predicting the behavior of these structures.

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Predictive analytics,Machine Learning,Data Mining,Big Data,Statistical analysis,Dam monitoring

• Computer and Information Sciences - Natural Sciences
• Civil Engineering - Engineering and Technology
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology