HYBRID MACHINE LEARNING MODELS FOR INTEGRATED URBAN AIR QUALITY AND WEATHER FORECASTING - A DECISION-SUPPORT FRAMEWORK FOR SUSTAINABLE CITY PLANNING

Rapid urbanization has intensified challenges related to air pollution and extreme weather, directly impacting public health and sustainable city planning. This study presents a hybrid machine learning framework for integrated air quality and weather forecasting using environmental, meteorological, and urban data. The model combines regression, classification, and time-series prediction techniques to capture complex interactions. Experimental results show improved air quality prediction (RMSE = 3.45) and weather forecasting accuracy (93.6%), demonstrating the framework’s effectiveness as a decision-support tool for climate-resilient and sustainable urban development.