Study of AQI Prediction using Recurrent Neural Network based Deep Learning Model with Hyperbolic Activation Function

Abstract

Investigating the application of sophisticated deep learning algorithms with the purpose of enhancing the accuracy of Air Quality Index (AQI) forecasts is the aim of this research project. Public health, environmental policy, urban planning, and other areas are impacted by the use of these strategies. By highlighting the vital necessity for precise air quality index (AQI) estimations in connection to public health, environmental policy, and urban planning, the research highlights the relevance of these concepts. Focusing primarily on hybrid models and Recurrent Neural Networks (RNNs), this research analyses the complexity of deep learning. It also offers a thorough summary of the most recent advancements in air quality index (AQI) prediction. The aim of this project is to determine the most effective transfer learning techniques and see how they might be used to the development of a better AQI prediction model. The provided model combines a Recurrent Neural Network (RNN) architecture with the Hyperbolic Activation Function (HAF). A thorough evaluation of the RNN-HAF model was conducted using a range of performance criteria, and the findings showed that the RNN-HAF model outperformed an existing deep learning model in the majority of the examined criteria. According to the study's findings, the RNN-HAF model performs better than other models already in use, which suggests that it might be a helpful tool for producing precise AQI predictions. Researching various normalizing and regularizing techniques, extending the model to multi-task environments, examining domain adaptation and transfer learning strategies, and incorporating explainable artificial intelligence techniques are some of the upcoming projects aimed at enhancing the model's interpretability.