Smart Aqi Forecasting: Real-Time Outdoor Safety and Health Advisory

Air pollution has emerged as one of the most pressing environmental and public health challenges across the globe, affecting millions of people on a daily basis. Rapid urbanization, industrial growth, increased vehicular emissions, and the widespread use of fossil fuels have contributed significantly to the deterioration of air quality. Poor air quality is directly linked to a range of health issues, including respiratory diseases, cardiovascular disorders, and reduced life expectancy. In this context, accurate prediction of the Air Quality Index (AQI) becomes essential, as it enables individuals, healthcare providers, and policymakers to take timely preventive measures. Furthermore, providing personalized health recommendations based on predicted air conditions can play a crucial role in raising public awareness and minimizing exposure to harmful pollutants. This paper proposes a comprehensive hybrid system that combines advanced deep learning techniques with interactive user support to address this issue. Specifically, the system integrates a Long Short-Term Memory (LSTM)-based model for AQI prediction with a lightweight chatbot assistant designed for real-time communication. LSTM networks, a specialized type of recurrent neural network, are particularly effective for time-series forecasting as they can capture long-term dependencies and temporal patterns in historical air quality data. Leveraging these capabilities, the model predicts AQI values for the upcoming seven days, allowing users to anticipate future air quality conditions with reasonable accuracy. In addition to prediction, the system emphasizes user-centric design by incorporating a chatbot that enhances accessibility and engagement. The chatbot interacts with users in real time, answering queries related to air quality and providing personalized health recommendations based on factors such as the user’s age and the predicted AQI levels. The chatbot is implemented using a rule-based natural language processing approach, ensuring quick responses and low computational overhead while maintaining contextual relevance. The proposed solution brings together machine learning, web-based technologies, and natural language interaction into a unified platform, resulting in an intuitive and user-friendly dashboard. Users can visualize AQI trends, access forecasts, and engage with the chatbot seamlessly through a single interface