(173m) A Novel Lstm-CNN Framework Forecasts India's Air Quality Using Historical Data, Outperforming Traditional Methods. Insights Aid Tailored Pollution Mitigation.

Air quality deterioration presents significant challenges in India, necessitating effective assessment and prediction methods. This study proposes integrating memory-based learning techniques to comprehensively evaluate air quality parameters across Indian regions. Utilizing LSTM and CNN, our framework effectively captures temporal and spatial dependencies using historical data. Validation against diverse pollutant datasets, including PM2.5, PM10, NO2, SO2, and O3, demonstrates its superiority over traditional methods. Furthermore, it explores the intricate relationship between meteorological factors and air quality, offering insights for targeted pollution mitigation. Utilizing data from monitoring stations, satellite imagery, and various sources, advanced analytics and machine learning algorithms are employed to develop a predictive model.Air quality deterioration presents significant challenges in India, necessitating effective assessment and prediction methods. This study proposes integrating memory-based learning techniques to comprehensively evaluate air quality parameters across Indian regions. Utilizing LSTM and CNN, our framework effectively captures temporal and spatial dependencies using historical data. Validation against diverse pollutant datasets, including PM2.5, PM10, NO2, SO2, and O3, demonstrates its superiority over traditional methods. Furthermore, it explores the intricate relationship between meteorological factors and air quality, offering insights for targeted pollution mitigation. Utilizing data from monitoring stations, satellite imagery, and various sources, advanced analytics and machine learning algorithms are employed to develop a predictive model. A novel framework merging LSTM and CNN effectively forecasts India's air quality using historical data. Validated against diverse pollutant datasets, it outperforms traditional methods. Insights into meteorological influences aid tailored pollution mitigation. Advanced analytics, including machine learning, utilize varied data sources to construct predictive models. Historical pollution and weather data drive training for regional pollution forecasts, promising targeted interventions for improved air quality management.In response to India's pressing air quality challenges, a pioneering approach intertwining long short-term memory (LSTM) networks with convolutional neural networks (CNN) has emerged. This innovative framework utilizes extensive historical air quality data to effectively capture both temporal and spatial dependencies. Rigorous validation against comprehensive multi-pollutant datasets, encompassing pollutants such as PM2.5, PM10, NO2, SO2, and O3, underscores its superior predictive capability over traditional methods. Beyond prediction, the framework unravels the complex relationship between meteorological variables and air quality variations, furnishing critical insights for tailored pollution mitigation strategies. Drawing from diverse data sources including monitoring stations and satellite imagery, advanced analytics techniques, particularly machine learning algorithms, are harnessed to construct a predictive model. Historical pollution records and meteorological parameters drive model training, facilitating forecasts of pollution levels across diverse Indian regions within specified timeframes. This holistic approach promises to inform targeted interventions for sustainable air quality management in India. effective evaluation and forecasting methods. Leveraging historical air quality data spanning several years, a novel framework is developed, combining long short-term memory (LSTM) networks with convolutional neural networks (CNN) to capture temporal and spatial dependencies effectively.through meticulous validation using comprehensive multi-pollutant datasets covering various pollutants such as particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3), we demonstrate the superior predictive performance of our proposed framework compared to conventional forecasting methods. Moreover, our analysis goes beyond mere prediction by delving into the intricate interplay between meteorological factors and air quality fluctuations, offering invaluable insights for the formulation and implementation of targeted pollution mitigation strategies Utilizing data from monitoring stations, satellite imagery, and various sources, advanced data analytics techniques, including machine learning algorithms, are employed to develop a predictive model. Historical pollution data and meteorological parameters serve as inputs to train the model, aiming to forecast pollution levels across different Indian regions over specific timeframes.