Hybrid artificial intelligence-based inference models for accurately predicting dam body displacements: A case study of the Fei Tsui dam

Abstract

Surveillance is a critical activity in monitoring the operation condition and safety of dams. This study reviewed the historical monitoring data of the Fei Tsui dam to determine possible influential factors for the dam body displacement and then evaluated the influencing degree of these factors by using correlation analysis. Thus, the key influential factors were identified objectively and further chosen as the input variables for numerous artificial intelligence (AI)-based inference models, including single machine learning techniques (support vector machine (SVM), artificial neural networks) and hybrid AI models. The models were trained and tested with 4722 real data retrieved in 11 years from the monitoring devices installed on elements of the dam, and then generated their respective inferred dam body displacement values. The results revealed that the adaptive time-dependent evolutionary least squares SVM model had the greatest performance by providing the lowest values of prediction errors in terms of mean absolute percentage error (MAPE = 8.14%), root mean square error (RMSE = 1.08 cm), and coefficient of determination (R = 0.993). The analysis results endorsed that the hybrid AI model could be an efficient tool to early produce accurate warnings of the dam displacements.