Predictive modeling of haze using chaos theory and deep learning algorithms
With the swift growth of urbanization and industrialization, fine particulate matter (PM$_{10}$) has escalated into a major global environmental crisis. PM$_{10}$ is often used as a haze indicator, severely affecting human health and ecosystem stability. Accurate prediction of PM$_{10}$ levels is crucial, but existing models face challenges in handling vast data and achieving high accuracy. This study investigates four years of PM$_{10}$ time series in industrial area in Malaysia. Paper aims to develop and compare haze predicting models using chaos theory, including