A Smart Post-Processing System for Forecasting the Climate Precipitation Based on Machine Learning Computations
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...
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| Published in | Sustainability Vol. 14; no. 11; p. 6624 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.06.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2071-1050 2071-1050 |
| DOI | 10.3390/su14116624 |
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| 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. |
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| AbstractList | 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. |
| Audience | Academic |
| Author | Gheibi, Mohammad Hajiaghaei-Keshteli, Mostafa Fathollahi-Fard, Amir M. Babaeian, Iman Ghazikhani, Adel |
| Author_xml | – sequence: 1 givenname: Adel orcidid: 0000-0003-2055-5209 surname: Ghazikhani fullname: Ghazikhani, Adel – sequence: 2 givenname: Iman surname: Babaeian fullname: Babaeian, Iman – sequence: 3 givenname: Mohammad surname: Gheibi fullname: Gheibi, Mohammad – sequence: 4 givenname: Mostafa orcidid: 0000-0002-9988-2626 surname: Hajiaghaei-Keshteli fullname: Hajiaghaei-Keshteli, Mostafa – sequence: 5 givenname: Amir M. orcidid: 0000-0002-5939-9795 surname: Fathollahi-Fard fullname: Fathollahi-Fard, Amir M. |
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| SubjectTerms | Adaptation Algorithms Case studies Climate change Climatic changes Droughts Germany Hydrology Iran Kalman filters Machine learning Meteorological research Methods Neural networks Precipitation Precipitation (Meteorology) Probability Rain Statistics Sustainability Sustainable development Taiwan Weather forecasting |
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| Title | A Smart Post-Processing System for Forecasting the Climate Precipitation Based on Machine Learning Computations |
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