Time series analysis on air quality and weather prediction

Abstract

Air quality and weather forecasting are two critical areas of research that have significant impacts on human health and the environment. Air pollution is a major concern in many urban areas, and its adverse effects on human health have been widely studied. Therefore, it is essential to develop accurate methods to predict air quality levels and provide early warnings to mitigate the effects of air pollution. Similarly, weather forecasting plays a crucial role in many fields, including agriculture, transportation, and disaster management. Accurate predictions of weather conditions can help prevent natural disasters, minimize their impact, and improve overall preparedness. In this project work, the main focus is on the use of time series analysis techniques to predict air quality index (AQI) and weather conditions. Specifically, we explore the application of linear regression, decision tree, and long short-term memory (LSTM) neural networks for AQI and weather forecasting. We analyze historical data from multiple sources, including government agencies and public repositories, to train and evaluate these models. Obtained results show that LSTM-based models outperform linear regression and decision tree models in terms of accuracy for both AQI and weather forecasting. We also demonstrate the importance of considering multiple factors in predicting AQI and weather conditions, as these factors can significantly impact the accuracy of the models. Additionally, we discuss the practical implications of our research, including the potential for early warning systems to be implemented based on our models to help mitigate the impact of air pollution and extreme weather conditions. Overall, this project work highlights the significance of time series analysis for AQI and weather prediction and demonstrates the effectiveness of LSTM-based models in accurately forecasting these variables. We believe that our findings can contribute to the development of more accurate and effective methods for predicting air quality and weather conditions, with potential applications in multiple fields, including public health, transportation, and disaster management.