Weather forecasting and prediction using hybrid C5.0 machine learning algorithm,International Journal of Communication Systems

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Weather forecasting and prediction using hybrid C5.0 machine learning algorithm
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2021-04-15 , DOI: 10.1002/dac.4805
Sudhan Murugan Bhagavathi ₁ , Anitha Thavasimuthu ₂ , Aruna Murugesan ₃ , Charlyn Pushpa Latha George Rajendran ₂ , Vijay A ₄ , Laxmi Raja ₅ , Rajendran Thavasimuthu ₅

Affiliation

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%.

中文翻译：

使用混合 C5.0 机器学习算法的天气预报和预测

本研究提出了一种基于机器学习的天气预报模型，以提高预报的准确性和效率。本研究的目的是提出一种基于数值数据的短程天气预报模型。每日天气预报包括全球数千名气象学家和观察员的工作。现代化的计算机使预测比以往任何时候都更加精确，地球轨道气象卫星从太空捕捉云的图片。但是，在很多情况下，很多条件下的预测是不准确的。数值天气预报（NWP）是预测天气状况的流行方法之一。NWP 是气象学家的主要天气建模工具，有助于提高准确度。本研究中，天气预报模型采用C5.0算法，K-均值聚类。C5.0 是更好的决策树分类器之一，决策树是预测和预测的绝佳替代方案。聚类K均值的算法用于将相同的数据组合在一起。对于这个过程，最初应用K- means 的聚类将数据集划分为最近的K聚类. 对于培训和测试，使用从数据库现代研究和应用历史分析 (MERRA) 中获得的气象数据收集。该模型的性能通过 MAE 平均绝对误差 (MAE) 和均方根误差 (RMSE) 进行评估。并且对所提出的模型进行了准确性、敏感性和特异性的评估以进行验证。将获得的结果与当前其他机器学习方法进行比较，所提出的模型实现了 90.18% 的预测准确率。

更新日期：2021-06-03

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