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

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.

Integrating Improved Coati Optimization Algorithm and Bidirectional Long Short-Term Memory Network for Advanced Fault Warning in Industrial Systems

“…This strategy is designed to prevent model overfitting and achieve data dimensionality reduction. The calculation formula for the Pearson correlation coefficient is shown in Equation (28).…”

“…Future endeavors could involve integrating our ICOA-Bi-LSTM strategy with other machine learning algorithms, such as support vector machines or decision trees, to bolster the model's robustness and predictive power [26,27]. Additionally, exploring ensemble learning methods like random forests or gradient boosting may further elevate the model's performance [28,29]. Lastly, the development of new optimization strategies for more effective parameter adjustments, including advanced hybrid meta-heuristic algorithms or the incorporation of additional optimization operators, presents exciting avenues for future research [30,31].…”

Hybrid optimization algorithm for enhanced performance and security of counter-flow shell and tube heat exchangers

“…Ghazikhani et al [ 32 ] a post-processing system based on machine learning was presented to improve the forecasting of climate precipitation. Using the random forest algorithm, regression techniques were applied to data from Climate Forecast System Version 2 (CFSV2).…”

Mining significant local spatial association rules for multi-category point data

“…Association rule mining is an important part of data mining [28] , [29] , [30] . Spatial association rule mining is significantly different from transaction-based association rule mining.…”