Comparative analysis of BPNN, SVR, LSTM, Random Forest, and LSTM-SVR for conditional simulation of non-Gaussian measured fluctuating wind pressures

Abstract

Non-Gaussian fluctuating wind pressure occurs in the separation zone of a structure, which will cause structural fatigue damage. The study of non-Gaussian wind pressure, accordingly, is essential, and the best way to study the non-Gaussian fluctuating wind pressure is the field measurement undoubtedly. However, an emergency such as sensor unavailability or sensor failure can lead to data missing. To this end, it is crucial to simulate the data at some unmeasured or missing points, and the multivariate conditional simulation can solve such problems. Although some scholars have conducted studies using SVM and BPNN, fewer studies use machine learning and deep learning to perform multivariate non-Gaussian conditional simulation. In addition to BPNN and SVM, there are some excellent prediction algorithms such as Long Short-Term Memory (LSTM), Random Forest (RF), and a combination of LSTM and SVR (LSTM-SVR). It should be noted that they can also simulate the time series with a small amount of data and obtain good predictions accuracy. Hence, different algorithms (BPNN, SVR, RF, LSTM, LSTM-SVR) are utilized to perform multivariate non-Gaussian wind pressure conditional simulation and comparative analysis using prediction measures (MAPE, RMSE, R). In this paper, the non-Gaussian characteristics of the wind pressure data measured in the field are analyzed first. Then different algorithms (BPNN, SVR, RF, LSTM, LSTM-SVR) are utilized to perform multivariate non-Gaussian wind pressure conditional simulation by spatial interpolation prediction, and their prediction performance measures (MAPE, RMSE, R) are compared. The comparison of prediction measures (MAPE, RMSE, R) reveals that the combined algorithm (LSTM-SVR) gets better accuracy than other algorithms. Finally, the multivariate non-Gaussian wind pressure series simulated by the combined algorithm (LSTM-SVR) are analyzed statistically and found to be consistent with the statistical properties (PSD, ACF, PDF) of the actual measured wind pressure data. The results indicate that the combined algorithm (LSTM-SVR) can utilize a small amount of data to realize the multivariate non-Gaussian conditional simulation by spatial interpolation prediction more accurately.