Robust and Accurate Weather Forecasting Using an Integrated Complex Cognitive Gradient Boosting Model

In the field of weather forecasting, integrating machine learning techniques, precisely complex gradient boosting and artificial neural networks (ANN) has led to a substantial amount of revolution. This paper looks into the collection and analysis of extensive meteorological data, emphasizing the necessity of selecting similar traits that substantially impact weather predictions. Complex gradient boosting and ANNs are effectively used to extract relevant information from the data that acts as a powerful tool for predicting extreme weather conditions. Simultaneously, it also examines how ANNs mimic the neural network of the brain while processing the complex patterns in the field of forecasting which detect changes in terms of temperature, humidity and pressure changes.