Application of time series prediction techniques for coastal bridge engineering

Yu, Enbo; Wei, Huan; Yan, Huang; Peng, Hu; Xu, Guoji

doi:10.1186/s43251-020-00025-4

Cited by 18 publications

(3 citation statements)

References 40 publications

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“…Statistical models have achieved good prediction results when dealing with smooth sequences, but wind speed sequences are more volatile and have obvious nonlinear features, so it is difficult to accurately predict complex changes using only statistical models. The autoregressive integrated moving average (ARIMA) model [16] is a classic model for wind speed prediction, and Yu et al [17] utilized an ARIMA model to predict the bounded non-periodic data in coastal bridges. Unlike statistical methods, machine learning methods can better fit the nonlinear characteristics of wind speed and can recognize prediction patterns in the presence of relationship uncertainty in historical data [18].…”

Section: Related Workmentioning

confidence: 99%

Short-Term Wind Speed Prediction for Bridge Site Area Based on Wavelet Denoising OOA-Transformer

Gao,

Cao,

et al. 2024

Mathematics

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Predicting wind speed in advance at bridge sites is essential for ensuring bridge construction safety under high wind conditions. This study proposes a short-term speed prediction model based on outlier correction, Wavelet Denoising, the Osprey Optimization Algorithm (OOA), and the Transformer model. The outliers caused by data entry and measurement errors are processed by the interquartile range (IQR) method. By comparing the performance of four different wavelets, the best-performing wavelet (Bior2.2) was selected to filter out sharp noise from the data processed by the IQR method. The OOA-Transformer model was utilized to forecast short-term wind speeds based on the filtered time series data. With OOA-Transformer, the seven hyperparameters of the Transformer model were optimized by the Osprey Optimization Algorithm to achieve better performance. Given the outstanding performance of LSTM and its variants in wind speed prediction, the OOA-Transformer model was compared with six other models using the actual wind speed data from the Xuefeng Lake Bridge dataset to validate our proposed model. The experimental results show that the mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) of this paper’s method on the test set were 4.16%, 0.0152, and 0.9955, respectively, which are superior to the other six models. The prediction accuracy was found to be high enough to meet the short-term wind speed prediction needs of practical projects.

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Section: Related Workmentioning

confidence: 99%

Short-Term Wind Speed Prediction for Bridge Site Area Based on Wavelet Denoising OOA-Transformer

Gao,

Cao,

et al. 2024

Mathematics

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“…SVR has been used in financial time series forecast and exchange rate forecasting [ 23 , 24 ]. GBM algorithm was applied to deal with the time series prediction tasks for coastal bridge engineering [ 25 ]. A random forest based regression model was developed in [ 26 ] to predict daily evapotranspiration from in-situ meteorological data and fluxes, satellite leaf area index (LAI), and land surface temperature data and found that the LAI is the most important feature.…”

Section: Introductionmentioning

confidence: 99%

Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India

et al. 2022

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Background Price forecasting of perishable crop like vegetables has importance implications to the farmers, traders as well as consumers. Timely and accurate forecast of the price helps the farmers switch between the alternative nearby markets to sale their produce and getting good prices. The farmers can use the information to make choices around the timing of marketing. For forecasting price of agricultural commodities, several statistical models have been applied in past but those models have their own limitations in terms of assumptions. Methods In recent times, Machine Learning (ML) techniques have been much successful in modeling time series data. Though, numerous empirical studies have shown that ML approaches outperform time series models in forecasting time series, but their application in forecasting vegetables prices in India is scared. In the present investigation, an attempt has been made to explore efficient ML algorithms e.g. Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machine (GBM) for forecasting wholesale price of Brinjal in seventeen major markets of Odisha, India. Results An empirical comparison of the predictive accuracies of different models with that of the usual stochastic model i.e. Autoregressive integrated moving average (ARIMA) model is carried out and it is observed that ML techniques particularly GRNN performs better in most of the cases. The superiority of the models is established by means of Model Confidence Set (MCS), and other accuracy measures such as Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Prediction Error (MAPE). To this end, Diebold-Mariano test is performed to test for the significant differences in predictive accuracy of different models. Conclusions Among the machine learning techniques, GRNN performs better in all the seventeen markets as compared to other techniques. RF performs at par with GRNN in four markets. The accuracies of other techniques such as SVR, GBM and ARIMA are not up to the mark.

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“…A novel model was proposed based on Extreme Learning Machine (ELM) and laboratory experiments to estimate the tsunami wave forces on coastal bridges [33]. The effects of three different machine learning techniques in predicting the wave loads on bridge decks were also compared [34]. It is proved that machine learning techniques can provide guidance for time-history prediction requirements.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Surrogate Model for the Prediction of Solitary Wave Forces on the Coastal Bridge Decks

Wang

Xue

2021

Infrastructures

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To facilitate the establishment of the probabilistic model for quantifying the vulnerability of coastal bridges to natural hazards and support the associated risk assessment and mitigation activities, it is imperative to develop an accurate and efficient method for wave forces prediction. With the fast development of computer science, surrogate modeling techniques have been commonly used as an effective alternative to computational fluid dynamics for the establishment of a predictive model in coastal engineering. In this paper, a hybrid surrogate model is proposed for the efficient and accurate prediction of the solitary wave forces acting on coastal bridge decks. The underlying idea of the proposed method is to enhance the prediction capability of the constructed model by introducing an additional surrogate to correct the errors made by the main predictor. Specifically, the regression-type polynomial chaos expansion (PCE) is employed as the main predictor to capture the global feature of the computational model, whereas the interpolation-type Kriging is adopted to learn the local variations of the prediction error from the PCE. An engineering case is employed to validate the effectiveness of the hybrid model, and it is observed that the prediction performance (in terms of residual mean square error and correlation coefficient) of the hybrid model is superior to the optimal PCE and artificial neural network (ANN) for both horizontal and vertical wave forces, albeit the maximum PCE degrees used in the hybrid model are lower than the optimal degrees identified in the pure PCE model. Moreover, the proposed hybrid model also enables the extraction of explicit predictive equations for the parameters of interest. It is expected that the hybrid model could be extended to more complex wave conditions and structural shapes to facilitate the life-cycle structural design and analysis of coastal bridges.

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Application of time series prediction techniques for coastal bridge engineering

Cited by 18 publications

References 40 publications

Short-Term Wind Speed Prediction for Bridge Site Area Based on Wavelet Denoising OOA-Transformer

Short-Term Wind Speed Prediction for Bridge Site Area Based on Wavelet Denoising OOA-Transformer

Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India

A Hybrid Surrogate Model for the Prediction of Solitary Wave Forces on the Coastal Bridge Decks

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