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Wind speed prediction and insight for generalized predictive modeling framework: a comparative study for different artificial intelligence models

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Abstract

Wind speed (WS) has played a vital role in local urban and sub-urban weather, agriculture, and ecosystem. Several meteorological parameters are influencing WS such as relative humidity (at 2 m, %), surface pressure (kPa), maximum temperature (at 2 m, °C), minimum temperature (at 2 m, °C), average temperature (at 2 m, °C), and all sky insolation incident on a horizontal surface (kW-h/m2/day). The current research was conducted to predict WS at different locations at Vietnam using the feasibility of computer aid models (i.e., multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost) and random forest generator (Ranger)). Pearson correlation (PC) was investigated to select the high significant predictors to predict the WS at 10 m high. All inputs (maximum number, 6) are chosen by the PC approach for PhuongNinh, DaNang, and HaNoi; and for minimum number of inputs i.e four, are selected for  PhuongHung, CanTho, and SaPa city; that exhibit the relationship with WS, citywise. The sequence selection of input parameters differed in each station as per the PC analysis. Based on the statistical evaluation and graphical presentation, MARS model attained the best prediction results, followed by XGBoost and Ranger. MARS predictive model remains at the top performance among others based on 95% confidence interval.

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Availability of data and materials

Data will be supplied upon request from corresponding author.

Abbreviations

ANFIS:

Adaptive Network-based Fuzzy Inference System

T :

Air temperature at 2 m

ASIIHS:

All Sky Insolation Incident on a Horizontal Surface in kW-h/m2/day

AI:

Artificial Intelligence

ANN:

Artificial Neural Network

ARMA:

Auto-regressive moving average

BP:

Back propagation

BPNN:

Back-Propagation neural network

R 2 :

Coefficient of determination

CI:

Confidence Interval

CNN:

Convolutional Neural Network

CNNSVM:

Convolutional Support Vector Machine

DBN:

Deep belief network

DEM:

Dynamic ensemble model

ENN:

Elman Neural Network

CEEMDAN-MOGOA:

Ensemble empirical mode decomposition-Multi-objective grasshopper optimization algorithm

XGBoost:

Extreme Gradient Boosting

ELM:

Extreme Learning Machine

GPR:

Gaussian Process Regression

GRNN:

General regression neural network

GCV:

Generalized Cross-Validation

GBDT:

Gradient boosting decision tree

HCMC:

Ho Chi Minh City

LSTM:

Long short-term memory network

T max :

Maximum air temperature at 2 m in °C

MAE:

Mean Absolute Error

MAPE:

Mean Absolute Percentage Error

MW:

Megawatt

T min :

Minimum air temperature at 2 m in °C

md:

Modified index of agreement

MARS:

Multivariate adaptive regression spline

Nash:

Nash-Sutcliffe efficiency

PC:

Pearson’s Correlation

RBF:

Radial basis function

RF:

Random Forest

RH:

Relative Humidity

RE:

Residual Error

RMSE:

Root-Mean-Squared Error

SW-LSTM:

Shared weight long short-term memory network

\(\sigma\) :

Standard deviation

SVM:

Support vector machine

SVR:

Support Vector Regression

PS:

Surface Pressure in kPa

VMD:

Variational mode decomposition

WP:

Wind power

WS:

Wind speed

WSTI-RNN:

Wind Speed and Turbulence Intensity-based Recursive Neural Network

WS:

Wind speed

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Acknowledgements

The authors reveal their appreciation and gratitude to the respected reviewers and editors for their constructive comments. Zaher Mundher Yaseen would like to appreciate the Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Saudi Arabia, for its support. Ms. Zainab Al-Khafaji acknowledges the support by Al-Mustaqbal University through the Grant Number: MUC-E-0122.

Funding

The research received no funds.

Author information

Authors and Affiliations

  1. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Suraj Kumar Bhagat

  2. Department of Computer Engineering Technology, Al Kitab University, Altun Kopru, Kirkuk, 00964, Iraq

    A. H. Shather

  3. Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Mehdi Jamei

  4. Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peters, PE, Canada

    Mehdi Jamei

  5. Building and Construction Techniques Engineering Department,College of Engineering and Engineering Techniques, Al-Mustaqbal University, Hillah, Babylon, 51001, Iraq

    Zainab Al-Khafaji

  6. Department of Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de Fora, Brazil

    Leonardo Goliatt

  7. Experimental Nuclear Radiation Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Iraq

    Shafik S. Shafik

  8. Department of Thermofluids, School of Mechanical Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor Bahru, Malaysia

    Omer A. Alawi

  9. Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia

    Zaher Mundher Yaseen

  10. Department of Civil Engineering, School of Engineering and Computing, Dev Bhoomi Uttarakhand University, Dehradun, India

    Tiyasha Tiyasha

  11. Dept of Environmental Science, Gitam School of Science, GITAM University, Visakhapatnam, 530045, India

    Adarsh Kumar

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Appendix A

Appendix A

The correlation statistics between the predictors and target and for all examined stations

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Bhagat, S.K., Tiyasha, T., Shather, A.H. et al. Wind speed prediction and insight for generalized predictive modeling framework: a comparative study for different artificial intelligence models. Neural Comput & Applic 36, 14119–14150 (2024). https://doi.org/10.1007/s00521-024-09677-z

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