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Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm
Author(s) -
Murugan Bhagavathi Sudhan,
Thavasimuthu Anitha,
Murugesan Aruna,
George Rajendran Charlyn Pushpa Latha,
A Vijay,
Raja Laxmi,
Thavasimuthu Rajendran
Publication year - 2021
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4805
Subject(s) - numerical weather prediction , computer science , weather forecasting , mean squared error , cluster analysis , machine learning , global forecast system , artificial intelligence , weather prediction , decision tree , predictive modelling , data mining , range (aeronautics) , algorithm , meteorology , statistics , mathematics , physics , materials science , composite material
Summary In this research, a weather forecasting model based on machine learning is proposed for improving the accuracy and efficiency of forecasting. The aim of this research is to propose a weather prediction model for short‐range prediction based on numerical data. Daily weather prediction includes the work of thousands of worldwide meteorologists and observers. Modernized computers make predictions more precise than ever, and earth‐orbiting weather satellites capture pictures of clouds from space. However, in many cases, the forecast under many conditions is not accurate. Numerical weather prediction (NWP) is one of the popular methods for forecasting weather conditions. NWP is a major weather modeling tool for meteorologists which contributes to more accurate accuracy. In this research, the weather forecasting model uses the C5.0 algorithm with K ‐means clustering. The C5.0 is one of the better decision tree classifiers, and the decision tree is a great alternative for forecasting and prediction. The algorithm for clustering the K ‐means is used to combine identical data together. For this process, the clustering of K ‐means is initially applied to divide the dataset into the closest cluster of K . For training and testing, the meteorological data collection obtained from the database Modern‐Era Historical Analysis for Research and Applications (MERRA) is used. The model's performance is assessed through MAE mean absolute error (MAE) and root mean square error (RMSE). And the proposed model is assessed with accuracy, sensitivity, and specificity for validation. The results obtained are compared with other current machine learning approaches, and the proposed model achieved predictive accuracy of 90.18%.