Enhancing solar radiation prediction accuracy: A hybrid machine learning approach integrating response surface method and support vector regression

Muhammad Adnan, Rana; Keshtegar, Behrooz; Abusurrah, Mona; Kisi, Ozgur; Alkabaa, Abdulaziz S.

doi:10.1016/j.asej.2024.103034

Ain Shams Engineering Journal

2024

DOI: 10.1016/j.asej.2024.103034

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Enhancing solar radiation prediction accuracy: A hybrid machine learning approach integrating response surface method and support vector regression

Rana Muhammad Adnan,

Behrooz Keshtegar,

Mona Abusurrah

et al.

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2024

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Enhanced Sequence-to-Sequence Attention-Based PM2.5 Concentration Forecasting Using Spatiotemporal Data

Kim,

Jung

et al. 2024

Atmosphere

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Severe air pollution problems continue to increase because of accelerated industrialization and urbanization. Specifically, fine particulate matter (PM2.5) causes respiratory and cardiovascular diseases, and according to the World Health Organization (WHO), millions of premature deaths and significant health burdens annually. Therefore, PM2.5 concentration forecasting is essential. This study proposed a method to forecast PM2.5 concentrations one hour after using Sequence-to-Sequence Attention (Seq2Seq-attention). The proposed method selects neighboring stations using minimum redundancy maximum relevance (mRMR) and integrates their data using a convolutional neural network (CNN). The proposed attention score and Seq2Seq are used on the integrated data to forecast PM2.5 concentration after one hour. The performance of the proposed method is validated through two case studies. The first comparison evaluated the performance of the conventional attention score against the proposed attention scores. The second comparison evaluated the forecasting results with and without considering neighboring stations. The first study showed that the proposed attention score improved the performance index (Root Mean Square Error (RMSE): 3.48%p, Mean Absolute Error (MAE): 8.60%p, R2: 0.49%p, relative Root Mean Square Error (rRMSE): 3.64%p, Percent Bias (PBIAS): 59.29%p). The second case study showed that considering neighboring stations’ data can be more effective in forecasting than considering that of a standalone station (RMSE: 5.49%p, MAE: 0.51%p, R2: 0.67%p, rRMSE: 5.44%p, PBIAS: 46.56%p). This confirmed that the proposed method can effectively forecast the PM2.5 concentration after one hour.

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Enhanced Sequence-to-Sequence Attention-Based PM2.5 Concentration Forecasting Using Spatiotemporal Data

Kim,

Jung

et al. 2024

Atmosphere

View full text Add to dashboard Cite

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Enhancing solar radiation prediction accuracy: A hybrid machine learning approach integrating response surface method and support vector regression

Cited by 1 publication

References 71 publications

Enhanced Sequence-to-Sequence Attention-Based PM2.5 Concentration Forecasting Using Spatiotemporal Data

Enhanced Sequence-to-Sequence Attention-Based PM2.5 Concentration Forecasting Using Spatiotemporal Data

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