Predictive modeling of haze using chaos theory and deep learning algorithms
With the swift growth of urbanization and industrialization, fine particulate matter (PM10) has escalated into a major global environmental crisis. PM10 is often used as a haze indicator, severely affecting human health and ecosystem stability. Accurate prediction of PM10 levels is crucial, but existing models face challenges in handling vast data and achieving high accuracy. This study investigates four years of PM10 time series in industrial area in Malaysia. Paper aims to develop and compare haze predicting models using chaos theory, including