Article
Version 1
Preserved in Portico This version is not peer-reviewed
Machine Learning-based Algorithms for Weather Forecasting
Version 1
: Received: 29 June 2022 / Approved: 30 June 2022 / Online: 30 June 2022 (11:30:04 CEST)
How to cite: oshodi, I. Machine Learning-based Algorithms for Weather Forecasting. Preprints 2022, 2022060428. https://doi.org/10.20944/preprints202206.0428.v1 oshodi, I. Machine Learning-based Algorithms for Weather Forecasting. Preprints 2022, 2022060428. https://doi.org/10.20944/preprints202206.0428.v1
Abstract
Weather forecast has a big impact on the global economy, accurate and timely weather forecast is required by all, it affects many aspects of human livelihood and lifestyle, it also plays a critical role in decision making for severe weather management and for primary and secondary sectors like agriculture, transportation, tourism, and industry as they rely on good weather conditions for production and operations. The erratic and uncertain complex nature of the weather makes traditional weather forecasting tedious and a challenging task, traditional weather forecast involves applying technology and scientific knowledge on numerical weather prediction (NWP), and weather radar to solve complex mathematical equations to obtain forecasts based on current weather conditions. These traditional processes utilize expensive, complex physical and computational power to produce forecasts, which can be inaccurate and have various catastrophic impacts on society. In this research, a machine learning-based weather forecasting model was proposed, the model was implemented using 4 classifier algorithms which include Random Forest classifier, Decision Tree Algorithm, Gaussian Naïve Bayes model, and Gradient Boosting Classifier, these algorithms were trained using a publicly available dataset from Kaggle for the city of Seattle for the period 2012 to 2015. The model’s performance was evaluated; the Gaussian Naive Bayes algorithm proved to be the best performing algorithm with a predictive accuracy of 84.153 %.
Keywords
machine learning; weather forecast
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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