A novel weather prediction model using a hybrid mechanism based on MLP and VAE with fire-fly optimization algorithm

The future weather data source will continue to grow rapidly, and new developments in machine learning would allow government agencies and companies to use all this data further. The weather prediction will never be flawless, but artificial intelligence (AI) can strive to enhance the exactness and consistency of the process. This paper proposes an approach using a hybrid mechanism based on multi-layer perceptron (MLP) and variational auto-encoder (VAE) with a fire-fly optimization mechanism. Weather-related data contains many features. A few of which are global or generalized features, and some are local or internal features. Single mechanism may not be effective in the process of extracting the specified features. Hence, a hybrid mechanism with the support of VAE and MLP is proposed to extract features and do classification. VAE is used to extract the global features from the weather data and the obtained or processed intermediately output given to the input as the MLP, which will extract all local or internal features very effectively.