Fusion Neural Networks for Air Pollution Prediction in Smart Cities

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"Fusion Neural Networks for Air Pollution Prediction in Smart Cities" by Sumaya Sanober explores the integration of artificial neural network models for precise air pollution forecasting in urban areas. Addressing the challenges posed by increasing urbanization and environmental issues, the book emphasizes the need for effective air quality management systems. The author investigates fusion techniques that combine various data sources and sensor networks to enhance the predictive power of neural networks. By integrating data from sensors measuring pollutants, weather conditions, and traffic patterns, the book illustrates how this fusion can provide a holistic view of air pollution dynamics in cities. Through comprehensive research, Sanober presents various fusion algorithms and model integration strategies that lead to the creation of accurate and robust prediction models. The work incorporates machine learning and deep learning methods alongside data fusion techniques to uncover significant patterns within extensive environmental datasets. The practical implications of these fusion models in smart cities are highlighted, underlining their role in aiding decision-making for air quality management, urban planning, and sustainability efforts. Additionally, the book underscores the necessity of real-time monitoring and data-driven strategies for effective pollution control and climate change mitigation. This resource is invaluab