Intelligent Air Pollution Forecasting: A Deep Learning Approach with 1D-CNN and Kookaburra Optimization

Air quality prediction is essential for environmental monitoring and reducing air pollution's health effects. Machine learning and deep learning models like 1D-CNN can forecast future air quality by studying data trends. Air quality predictions are crucial for environmental monitoring and public health. Traditional ML models often miss complex geographical and temporal relationships in air quality data. To demonstrate a kookaburra-optimized one-dimensional convolutional neural network (1D-CNN) to improve prediction efficiency and accuracy. The 1D-CNN model extracts important properties to accurately capture time-series air quality data trends and swings. The kookaburra optimization method dynamically fine-tunes crucial model parameters for optimal network architecture and decreased computational costs. Experimental results reveal that the 1D-CNN with kookaburra optimization outperforms traditional models in accuracy, prediction error, and generalization across different air quality datasets. The findings show that deep learning and nature-inspired optimisation can be used in green computing. Future research can integrate attention processes, hybrid deep learning architectures, and real-time IoT sensor networks to improve air quality forecasts. The framework promotes intelligent air quality monitoring systems by enabling proactive pollution control and public health safety decisions.