APPLICATION OF WEATHER PREDICTION TOOL USING ARTIFICIAL NEURAL NETWORK
DOI:
https://doi.org/10.29121/shodhkosh.v5.i2.2024.1960Keywords:
Predictive Analytics, ANN, Regression Techniques, Machine Learning TechniquesAbstract [English]
In the modern world, weather forecasting is a vital occurrence. Even if weather prediction is entirely automated and made possible by technologies like Weather Research & Forecasting (WRF), Advanced Research WRF (ARW), and Weather Processing System (WPS), it's still a difficult and interesting topic because forecasts aren't always accurate. The process of forecasting weather is complex, dynamic, high-dimensional, and ongoing since it incorporates a wide range of atmospheric phenomena. The intricacy of the criteria needed to forecast the weather means that even short-term forecasts are problematic. Artificial neural networks are perfect for weather forecasting because of their ability to learn from past data in addition to analyzing it and generate forecasts for the future. It is possible to simplify weather forecasting. By using the data gathered at a certain station over a predetermined period of time to train artificial neural networks (ANN) with back propagation for supervised learning. They are used to forecast the weather after the model has been trained. The model is revealed to anticipate the values as unknown values as an experimental technique. The results show promise and encourage us to keep working toward this objective.
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