Weather forecasting: unleashing the power of AI, machine learning, and data science. Before AI, weather forecasting was done by numerical weather forecast models. These models are based on physics-based equations and are used to simulate atmospheric processes and generate forecasts.Artificial Intelligence (AI) is a rapidly emerging field within computer technology, with the potential to significantly alter facets of society as we know it. AI methods, applied to the data in automatic fashion, identify and mine complex and intricate relationships between this unstructured and heterogeneous data without calling upon a clear analytical handling of these relationships. These AI methods are essential to understand the exponentially accelerating data flood and to react to the tougher new.Machine Learning (ML) is a new area introduced and applied in the area of atmospheric science. Weather information is calculated by ML, under the support and accessible data of the relative predictors. Consolidated ML and predictive modelling results from which we can derive more concrete data results on how ML enhances the physically grounded models.As the weather data is nonlinear and follows a very irregular pattern, Artificial Neural Network (ANN) has turned out to be a better technique to induce the structural relationship between the various entities. The paper explores the Artificial Neural Network (ANN) approach by developing efficient and accurately 365 days’ maximum, temperature prediction model for weather analysis and also compares and evaluate the performance of the developed models using different transfer functions, hidden layers and neurons.
Weather forecasting, artificial intelligence, machine learning, neural networks, deep learning, big data analytics.
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