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Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning

Author

Listed:
  • Isa Ebtehaj

    (Department of Soils and Agri-Food Engineering, Université Laval, Québec, QC G1V 0A6, Canada)

  • Keyvan Soltani

    (Department of Civil Engineering, Razi University, Kermanshah 6714967346, Iran)

  • Afshin Amiri

    (Department of Remote Sensing and GIS, University of Tehran, Tehran 1417935840, Iran)

  • Marzban Faramarzi

    (Rangeland and Watershed Management Group, Faculty of Agriculture, Ilam University, Ilam 69315516, Iran)

  • Chandra A. Madramootoo

    (Department of Bioresource Engineering, McGill University, Quebec, QC H9X 3V9, Canada)

  • Hossein Bonakdari

    (Department of Soils and Agri-Food Engineering, Université Laval, Québec, QC G1V 0A6, Canada)

Abstract

Shortwave radiation density flux (SRDF) modeling can be key in estimating actual evapotranspiration in plants. SRDF is the result of the specific and scattered reflection of shortwave radiation by the underlying surface. SRDF can have profound effects on some plant biophysical processes such as photosynthesis and land surface energy budgets. Since it is the main energy source for most atmospheric phenomena, SRDF is also widely used in numerical weather forecasting. In the current study, an improved version of the extreme learning machine was developed for SRDF forecasting using the historical value of this variable. To do that, the SRDF through 1981–2019 was extracted by developing JavaScript-based coding in the Google Earth Engine. The most important lags were found using the auto-correlation function and defined fifteen input combinations to model SRDF using the improved extreme learning machine (IELM). The performance of the developed model is evaluated based on the correlation coefficient (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe efficiency (NSE). The shortwave radiation was developed for two time ahead forecasting (R = 0.986, RMSE = 21.11, MAPE = 8.68%, NSE = 0.97). Additionally, the estimation uncertainty of the developed improved extreme learning machine is quantified and compared with classical ELM and found to be the least with a value of ±3.64 compared to ±6.9 for the classical extreme learning machine. IELM not only overcomes the limitation of the classical extreme learning machine in random adjusting of bias of hidden neurons and input weights but also provides a simple matrix-based method for practical tasks so that there is no need to have any knowledge of the improved extreme learning machine to use it.

Suggested Citation

  • Isa Ebtehaj & Keyvan Soltani & Afshin Amiri & Marzban Faramarzi & Chandra A. Madramootoo & Hossein Bonakdari, 2021. "Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning," Sustainability, MDPI, vol. 13(14), pages 1-23, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:8009-:d:596374
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    References listed on IDEAS

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    1. Han Khanh Nguyen, 2021. "Application of Mathematical Models to Assess the Impact of the COVID-19 Pandemic on Logistics Businesses and Recovery Solutions for Sustainable Development," Mathematics, MDPI, vol. 9(16), pages 1-21, August.

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