PriMera Scientific Engineering (ISSN: 2834-2550)

Review Article

Volume 3 Issue 3

Big Data Analytics in Weather Forecasting using Gradient Boosting classifiers Algorithm

Kamel Maaloul* and Brahim Lejdel

August 18, 2023

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Abstract

Weather forecasting, a crucial and vital process in people's everyday lives, assesses the change taking place in the atmosphere's current state. Big data analytics is the practice of studying big data to uncover hidden patterns and useful information that might produce more beneficial outcomes. Big data is currently a topic of fascination for many facets of society, and the meteorological institute is no exception. Big data analytics will therefore produce better results for weather forecasting and assist forecasters in providing more accurate weather predictions. Several big data techniques and technologies have been proposed to manage and evaluate the enormous volume of weather data from various resources in order to accomplish this goal and to identify beneficial solutions. A smart city is a project that uses computers to process vast amounts of data gathered from sensors, cameras, and other devices in order to manage resources, provide services, and address problems that arise in daily life, such as the weather. Forecasting the weather is a crucial process in daily life because it assesses changes in the atmosphere's current state. A machine learning-based weather forecasting model was proposed in this paper, and it was implemented using 5 classifier algorithms, including the Random Forest classifier, the Decision Tree Algorithm, the Gaussian Naive Bayes model, the Gradient Boosting Classifier, and Artificial Neural Networks. These classifier algorithms were trained using a publicly available dataset. When the model's performance was assessed, the Gradient Boosting Classifier algorithm, which had a plus 98% predicted accuracy, came out on top.

Keywords: Weather forecasting; Big data; Machine Learning; smart city; Gradient Boosting Classifier

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