Weather Forecasting Model using Artificial Neural Network

The objective of this research is to improve the efficiency and accuracy of weather forecastingthrough the use of artificial neural networks (ANNs). Conventional weather forecasting techniquesfrequently rely on intricate numerical models that need large amounts of processing power. On the otherhand, artificial neural networks (ANNs) have the ability to learn from past meteorological data, spottingpatterns and connections that traditional methods might miss. A multi-layer perceptron (MLP) architectureis used in this study. The MLP architecture was trained using datasets that included temperature, humidity,and air pressure throughout a range of time periods. When compared to common meteorologicalbenchmarks, the model's performance shows a notable improvement in the accuracy of short-termforecasts. The findings show that ANNs are capable of producing accurate and timely forecasts byefficiently capturing the nonlinear dynamics of weather systems. This method provides a scalable solutionfor real-time applications while simultaneously lowering processing demands. In the end, the results showhow machine learning methods may be incorporated into meteorology to create more intelligent andadaptive weather forecast systems.