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

Adel Ghazikhani, Iman Babaeian, Mohammad Gheibi, Mostafa Hajiaghaei-Keshteli and Amir M. Fathollahi-Fard
Additional contact information
Adel Ghazikhani: Department of Computer Engineering, 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

Sustainability, 2022, vol. 14, issue 11, 1-27

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.

Keywords: CFSV2; post-processing; regression; random forest; decision support system; sustainable development goals (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/11/6624/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/11/6624/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:11:p:6624-:d:826645

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().