Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction

Abstract

The main aim of structural safety control is the multiple assessments of the expected dam behaviour based on models and the measurements and parameters that characterise the dam’s response and condition. In recent years, there is an increase in the use of data-based models for the analysis and interpretation of the structural behaviour of dams. Multiple Linear Regression is the conventional, widely used approach in dam engineering, although interesting results have been published based on machine learning algorithms such as artificial neural networks, support vector machines, random forest, and boosted regression trees. However, these models need to be carefully developed and properly assessed before their application in practice. This is even more relevant when an increase in users of machine learning models is expected. For this reason, this paper presents extensive work regarding the verification and validation of data-based models for the analysis and interpretation of observed dam’s behaviour. This is presented by means of the development of several machine learning models to interpret horizontal displacements in an arch dam in operation. Several validation techniques are applied, including historical data validation, sensitivity analysis, and predictive validation. The results are discussed and conclusions are drawn regarding the practical application of data-based models.

Soft Computing and Machine Learning in Dam Engineering

“…However, validating these models based on the dam engineer's knowledge is fundamental for their adequate use. This issue is tackled in the paper by Mata et al [45] "Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction". The authors present a methodology based on several validation techniques, including historical data validation, sensitivity analysis, and predictive validation for the practical application of data-based models for structural dam behavior prediction in daily dam surveillance activities.…”

Dam deformation forecasting using SVM-DEGWO algorithm based on phase space reconstruction

“…However, conventional models are difficult to adapt to the complex nonlinear relationship between multi-factors and effect sizes, and the accuracy of model predictions is hard to guarantee. With the development of machine learning (ML) technology, ML-based models are widely used to explain the structural behavior of dams [ 6 ]. Moreover, the existing prediction models only consider the main influencing factors, such as water pressure, temperature and aging, but do not consider the chaotic components that may be included in the deformed time series, which further limits the improvement of fitting accuracy [ 7 ].…”

A Machine Learning-Based Surrogate Model for the Identification of Risk Zones Due to Off-Stream Reservoir Failure

“…In another study, Hooshyaripor et al [15] reported higher accuracy on a synthetic dataset generated by means of copulas based on the independent relationship between height and volume of water in the reservoirs. ML algorithms have also been applied to other dam safety problems, such as the prediction of dam behaviour [16,17], the detection of anomalies [18][19][20] based on monitoring data, or the prediction of flood maps [21,22].…”