Authors : Dr. M. M Raghuwanshi; Yogesh Katre; Ayushi Sahu; Disha Sharma; Ankush Udapure; Chaitanya Lonarkar

Volume/Issue : Volume 9 - 2024, Issue 9 - September

Google Scholar : https://tinyurl.com/4zmasyp8

Scribd : https://tinyurl.com/rcuep7rs

DOI : https://doi.org/10.38124/ijisrt/IJISRT24SEP1682

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Weather forecasting is the technical process of predicting atmospheric conditions at a location. Many scientists have also been trying to predict the weather, both formally and informally, for years. Weather forecasting is done by collecting data from specific locations with different climate attributes. Traditional numerical weather prediction models have made significant progress, but there are still limitations in their accuracy, especially in detecting local weather events. In recent years, machine learning techniques have become a powerful tool for improving weather forecasts by exploiting the large amounts of data and complex patterns inherent in atmospheric systems. We propose a machine- learning model that uses historical data to train the model. The model is then used to predict weather with better accuracy than traditional models.

Keywords : Weather Forecast, ANN, Machine Learning, Deep Learning.

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