Wavelet kernel least square twin support vector regression for wind speed prediction

Hazarika, Barenya Bikash; Gupta, Deepak; Natarajan, N.

doi:10.1007/s11356-022-18655-8

Cited by 16 publications

(3 citation statements)

References 53 publications

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“…In this study, the non-linear SVM model was chosen to cope with the large dimensionality and the complexity of the classification situation. The non-linear SVM [26] classification problem cannot find an optimal classification hyperplane on a space of low dimensionality; as such, each class of number sets was separated entirely and correctly, requiring the introduction of mapping at this point: x → Φ(x) . All points in the original space were mapped into the higher dimensional space and solved.…”

Section: Support Vector Machinementioning

confidence: 99%

A Methodology for Predicting Ground Delay Program Incidence through Machine Learning

Dong

Zhu

et al. 2023

Sustainability

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Effective ground delay programs (GDP) are needed to intervene when there are bad weather or airport capacity issues. This paper proposes a new methodology for predicting the incidence of effective ground delay programs by utilizing machine learning techniques, which can improve the safety and economic benefits of flights. We use the combination of local weather and flight operation data along with the ATM airport performance (ATMAP) algorithm to quantify the weather and to generate an ATMAP score. We then compared the accuracy of three machine learning models, Support Vector Machine, Random Forest, and XGBoost, to estimate the probability of GDPs. The results of the weather analysis, performed by the ATMAP algorithm, indicated that the ceiling was the most critical weather factor. Lastly, we used two linear regression models (ridge and LASSO) and a non-linear regression model (decision tree) to predict departure flight delays during GDP. The predictive accuracy of the regression models was enhanced by an increase in ATMAP scores, with the decision tree model outperforming the other models, resulting in an improvement of 8.8% in its correlation coefficient (R2).

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Section: Support Vector Machinementioning

confidence: 99%

A Methodology for Predicting Ground Delay Program Incidence through Machine Learning

Dong

Zhu

et al. 2023

Sustainability

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“…At present, the TWSVM model has been widely used in materials, machinery, electric power, and other fields, but it has not been used much in surface deformation prediction of open-pit slope, dam body, bridge, and other structures. In TWSVM, penalty factor and kernel function are the two most important parameters [15], which affect the computing power and the modelling effect of the model. Therefore, parameter optimisation of these two parameters is very critical, and it is necessary to use intelligent algorithms for parameter optimisation to improve the prediction effect [16].…”

Section: Introductionmentioning

confidence: 99%

Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

Du,

Song,

et al. 2024

ELEKTRON ELEKTROTECH

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At present, due to the complex and changeable geological conditions, the precise deformation prediction technology of high and steep slope could not achieve an accurate prediction. In particular, the single forecasting model has some problems such as poor stability, low precision, and data fluctuation. In practice, excavating the complex nonlinear relationship between open-pit slope surface deformation monitoring data and various influencing factors and improving the accuracy of the deformation prediction of high and steep slopes is the key to safe open-pit mine production. It proposed to introduce the position factor and the velocity factor into a twin support vector machine (TWSVM). The adaptive subgroup optimisation (APSO) algorithm is selected for parameter optimisation. Through the comparative analysis of TWSVM, genetic algorithm-TWSVM (GA-TWSVM), and the proposed APSO⁃TWSVM, the experimental data show that the mean absolute error (MAE) values of the three models are 13.29 %，8.17 %, and 1.27 %, the RMSE - 47.83 %，6.52 %, and 3.02 %, respectively; the prediction time for APSO⁃TWSVM is improved by 62.5 % compared to GA-TWSVM.

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“…Emerging from the use of the wavelet kernel, the wavelet kernel with the support vector machines [6] has been shown to be quite effective in the approximation process of non-linear functions in order to obtain, in some cases, a predictive capability superior to that of the radial basis function (RBF) kernel [7] in a wide range of applications, such as economics [8], wind speed prediction [9], and carbon price prediction [10]. Examples of its use are commonly seen in conjunction with neural networks for classification problems as a way to improve the predictive capacity of the model [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Support Vector Censored Regression

et al. 2023

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Learning methods in survival analysis have the ability to handle censored observations. The Cox model is a predictive prevalent statistical technique for survival analysis, but its use rests on the strong assumption of hazard proportionality, which can be challenging to verify, particularly when working with non-linearity and high-dimensional data. Therefore, it may be necessary to consider a more flexible and generalizable approach, such as support vector machines. This paper aims to propose a new method, namely wavelet support vector censored regression, and compare the Cox model with traditional support vector regression and traditional support vector regression for censored data models, survival models based on support vector machines. In addition, to evaluate the effectiveness of different kernel functions in the support vector censored regression approach to survival data, we conducted a series of simulations with varying number of observations and ratios of censored data. Based on the simulation results, we found that the wavelet support vector censored regression outperformed the other methods in terms of the C-index. The evaluation was performed on simulations, survival benchmarking datasets and in a biomedical real application.

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Wavelet kernel least square twin support vector regression for wind speed prediction

Cited by 16 publications

References 53 publications

A Methodology for Predicting Ground Delay Program Incidence through Machine Learning

A Methodology for Predicting Ground Delay Program Incidence through Machine Learning

Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

Wavelet Support Vector Censored Regression

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