Anomaly Detection in Dam Behaviour with Machine Learning Classification Models

Water

卷:13

David J. Vicente¹  Joaquín Irazábal¹  André Conde¹  Fernando Salazar¹ 

[1] International Centre for Numerical Methods in Engineering (CIMNE), Universitat Politècnica de Catalunya, 08034 Barcelona, Spain;

关键词: anomaly detection;    machine learning;    support vector machines;    random forest;    one-class classification;   

DOI  :  10.3390/w13172387

来源: DOAJ

【 摘 要 】

Dam safety assessment is typically made by comparison between the outcome of some predictive model and measured monitoring data. This is done separately for each response variable, and the results are later interpreted before decision making. In this work, three approaches based on machine learning classifiers are evaluated for the joint analysis of a set of monitoring variables: multi-class, two-class and one-class classification. Support vector machines are applied to all prediction tasks, and random forest is also used for multi-class and two-class. The results show high accuracy for multi-class classification, although the approach has limitations for practical use. The performance in two-class classification is strongly dependent on the features of the anomalies to detect and their similarity to those used for model fitting. The one-class classification model based on support vector machines showed high prediction accuracy, while avoiding the need for correctly selecting and modelling the potential anomalies. A criterion for anomaly detection based on model predictions is defined, which results in a decrease in the misclassification rate. The possibilities and limitations of all three approaches for practical use are discussed.

【 授权许可】

Unknown