A Legal Optimized Hybrid Deep Learning Models for Enhanced Real-Time Air Quality Prediction and Environmental Monitoring Using LSTM and CNN Architectures

Abstract

This paper proposed some innovative hybrid models integrating Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to enhance air quality prediction in real-time. It helps the limitations due to dynamic and non-linear environmental data that require accurate and up-to-date prediction models for managing air quality and provides a significant potential in theory of environment. This hybrid model incorporates CNN to learn spatial dependencies and LSTM are used for capturing the long term and short term temporal sequences, together it makes a more effective analysis of air pollution trends. The model in this study was applied on global scenarios that have crucial ecological data (e. g., PM 2.5) in large-scale environmental datasets. PM5-P10-CO-NOx (real-time from monitoring systems) The enhanced prediction accuracy and computational efficiency of the proposed model over conventional approaches were substantiated through a number of optimisation strategies such as hyperparameter tuning and model regularisation. The research also shows other important indexes to test model performance, such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Furthermore, the paper describes how the model can be deployed in a real-time scenario that highlights both its scale and integration with existing air quality monitoring systems. The results have shown that in addition to improved prediction performance the hybrid LSTM-CNN model is able to show support for the large and noisy nature of the real datasets, making it a viable candidate of effective tool for environmental monitoring applications and decision-making.