Open Access

ARTICLE

Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

Hadil Shaiba¹, Radwa Marzouk², Mohamed K Nour³, Noha Negm^4,5, Anwer Mustafa Hilal^6,*, Abdullah Mohamed⁷, Abdelwahed Motwakel⁶, Ishfaq Yaseen⁶, Abu Sarwar Zamani⁶, Mohammed Rizwanullah⁶

1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
3 Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Saudi  Arabia
4 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
5 Faculty of Science, Mathematics and Computer Science Department, Menoufia University, Egypt
6 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
7 Research Centre, Future University in Egypt, New Cairo, 11845, Egypt

* Corresponding Author: Anwer Mustafa Hilal. Email:

Computers, Materials & Continua 2022, 73(2), 3367-3382. https://doi.org/10.32604/cmc.2022.030067

Received 17 March 2022; Accepted 19 April 2022; Issue published 16 June 2022

Abstract

The agricultural sector’s day-to-day operations, such as irrigation and sowing, are impacted by the weather. Therefore, weather constitutes a key role in all regular human activities. Weather forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from disasters. Rainfall, wind speed, humidity, wind direction, cloud, temperature, and other weather forecasting variables are used in this work for weather prediction. Many research works have been conducted on weather forecasting. The drawbacks of existing approaches are that they are less effective, inaccurate, and time-consuming. To overcome these issues, this paper proposes an enhanced and reliable weather forecasting technique. As well as developing weather forecasting in remote areas. Weather data analysis and machine learning techniques, such as Gradient Boosting Decision Tree, Random Forest, Naive Bayes Bernoulli, and KNN Algorithm are deployed to anticipate weather conditions. A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather forecasting. The aim of this study is to demonstrate its ability to predict weather forecasts as soon as possible. Experimental evaluation shows our ensemble technique achieves 95% prediction accuracy. Also, for 1000 nodes it is less than 10 s for prediction, and for 5000 nodes it takes less than 40 s for prediction.

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Keywords

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