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

• 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
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su14116624

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.