IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i11p6624-d826645.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/11/6624/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/11/6624/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. 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.
    3. 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.
    4. 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.
    5. Amir M. Fathollahi-Fard & Abbas Ahmadi & Behrooz Karimi, 2021. "Multi-Objective Optimization of Home Healthcare with Working-Time Balancing and Care Continuity," Sustainability, MDPI, vol. 13(22), pages 1-33, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    2. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    3. Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    4. Asghari, Mohammad & Jaber, Mohamad Y. & Mirzapour Al-e-hashem, S.M.J., 2023. "Coordinating vessel recovery actions: Analysis of disruption management in a liner shipping service," European Journal of Operational Research, Elsevier, vol. 307(2), pages 627-644.
    5. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    6. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
    7. Norah Alyabs & Sy Han Chiou, 2022. "The Missing Indicator Approach for Accelerated Failure Time Model with Covariates Subject to Limits of Detection," Stats, MDPI, vol. 5(2), pages 1-13, May.
    8. Feldkircher, Martin, 2014. "The determinants of vulnerability to the global financial crisis 2008 to 2009: Credit growth and other sources of risk," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 19-49.
    9. Eunsil Seok & Akhgar Ghassabian & Yuyan Wang & Mengling Liu, 2024. "Statistical Methods for Modeling Exposure Variables Subject to Limit of Detection," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 435-458, July.
    10. Ida Kubiszewski & Kenneth Mulder & Diane Jarvis & Robert Costanza, 2022. "Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 139-148, February.
    11. Georges Steffgen & Philipp E. Sischka & Martha Fernandez de Henestrosa, 2020. "The Quality of Work Index and the Quality of Employment Index: A Multidimensional Approach of Job Quality and Its Links to Well-Being at Work," IJERPH, MDPI, vol. 17(21), pages 1-31, October.
    12. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    13. Esef Hakan Toytok & Sungur Gürel, 2019. "Does Project Children’s University Increase Academic Self-Efficacy in 6th Graders? A Weak Experimental Design," Sustainability, MDPI, vol. 11(3), pages 1-12, February.
    14. J M van Niekerk & M C Vos & A Stein & L M A Braakman-Jansen & A F Voor in ‘t holt & J E W C van Gemert-Pijnen, 2020. "Risk factors for surgical site infections using a data-driven approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    15. Joost R. Ginkel, 2020. "Standardized Regression Coefficients and Newly Proposed Estimators for $${R}^{{2}}$$R2 in Multiply Imputed Data," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 185-205, March.
    16. Lara Jehi & Xinge Ji & Alex Milinovich & Serpil Erzurum & Amy Merlino & Steve Gordon & James B Young & Michael W Kattan, 2020. "Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
    17. Matthew Carli & Mary H. Ward & Catherine Metayer & David C. Wheeler, 2022. "Imputation of Below Detection Limit Missing Data in Chemical Mixture Analysis with Bayesian Group Index Regression," IJERPH, MDPI, vol. 19(3), pages 1-17, January.
    18. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    19. Tsai, Tsung-Han, 2016. "A Bayesian Approach to Dynamic Panel Models with Endogenous Rarely Changing Variables," Political Science Research and Methods, Cambridge University Press, vol. 4(3), pages 595-620, September.
    20. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6624-:d:826645. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.