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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Bibliographic Details
Published in:Sustainability 2022-06, Vol.14 (11), p.6624
Main Authors: Ghazikhani, Adel, Babaeian, Iman, Gheibi, Mohammad, Hajiaghaei-Keshteli, Mostafa, Fathollahi-Fard, Amir M
Format: Article
Language:English
Subjects:
Adaptation
Algorithms
Case studies
Climate change
Climate models
Climatic changes
Correlation coefficient
Correlation coefficients
Drought
Droughts
Environmental protection
Germany
Hydrology
Iran
Kalman filters
Learning algorithms
Machine learning
Meteorological research
Methods
Neural networks
Precipitation
Precipitation (Meteorology)
Prediction models
Probability
Rain
Statistics
Sustainability
Sustainable development
Taiwan
Weather forecasting
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su14116624