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A Smart Post-Processing System for Forecasting the Climate Precipitation Based on Machine Learning Computations

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  • Adel Ghazikhani

    (Department of Computer Engineering, Imam Reza International University, Mashhad 178-436, Iran
    Big Data Lab, Imam Reza International University, Mashhad 178-436, Iran)

  • Iman Babaeian

    (Climatological Research Institute, Mashhad 154-329, Iran)

  • Mohammad Gheibi

    (Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Puebla 6500, Mexico)

  • Mostafa Hajiaghaei-Keshteli

    (Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Puebla 6500, Mexico)

  • Amir M. Fathollahi-Fard

    (Department of Electrical Engineering, École de Technologie Supérieure, University of Québec, Montréal, QC H3C 1K3, Canada)

Abstract

Although many meteorological prediction models have been developed recently, their accuracy is still unreliable. Post-processing is a task for improving meteorological predictions. This study proposes a post-processing method for the Climate Forecast System Version 2 (CFSV2) model. The applicability of the proposed method is shown in Iran for observation data from 1982 to 2017. This study designs software to perform post-processing in meteorological organizations automatically. From another point of view, this study presents a decision support system (DSS) for controlling precipitation-based natural side effects such as flood disasters or drought phenomena. It goes without saying that the proposed DSS model can meet sustainable development goals (SDGs) with regards to a grantee of human health and environmental protection issues. The present study, for the first time, implemented a platform based on a graphical user interface due to the prediction of precipitation with the application of machine learning computations. The present research developed an academic idea into an industrial tool. The final finding of this paper is to introduce a set of efficient machine learning computations where the random forest (RF) algorithm has a great level of accuracy with more than a 0.87 correlation coefficient compared with other machine learning methods.

Suggested Citation

  • Adel Ghazikhani & Iman Babaeian & Mohammad Gheibi & Mostafa Hajiaghaei-Keshteli & Amir M. Fathollahi-Fard, 2022. "A Smart Post-Processing System for Forecasting the Climate Precipitation Based on Machine Learning Computations," Sustainability, MDPI, vol. 14(11), pages 1-27, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6624-:d:826645
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    References listed on IDEAS

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    3. Roman Rudenko & Ivan Miguel Pires & Margarida Liberato & João Barroso & Arsénio Reis, 2022. "A Brief Review on 4D Weather Visualization," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
    4. Mohammad Asghari & Amir M. Fathollahi-Fard & S. M. J. Mirzapour Al-e-hashem & Maxim A. Dulebenets, 2022. "Transformation and Linearization Techniques in Optimization: A State-of-the-Art Survey," Mathematics, MDPI, vol. 10(2), pages 1-26, January.
    5. Michael Scheuerer & Luca Büermann, 2014. "Spatially adaptive post-processing of ensemble forecasts for temperature," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(3), pages 405-422, April.
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