Technological Applications of AI in the Development Sustainable Future

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Bol Technological Applications of AI in the Development of a Sustainable Future provides a comprehensive, interdisciplinary perspective on the integration of AI technologies with renewable energy, smart infrastructure, environmental monitoring, and sustainable industrial practices. The book balances theory and practical application with chapters covering AI-driven solutions in energy optimisation, climate modelling, smart grids, precision agriculture, waste management, water resource management, and circular economy initiatives. Emerging AI techniques, including machine learning, deep learning, reinforcement learning, and predictive analytics, are examined for their potential to reduce environmental impact, enhance efficiency, and support data-driven decision-making in sustainability-focused domains. Case studies and real-world examples illustrate how AI applications are transforming industries and communities toward a greener, more resilient future. Key Features: -Provides interdisciplinary insights into connecting AI, environmental science, and sustainable development. -Real-world case studies demonstrating AI applications for renewable energy, smart infrastructure, and resource management. -Discusses ethical, social, and policy considerations in AI-enabled sustainability solutions. -Examines the impact of emerging AI techniques and their practical implications on sustainable practices.

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Technological Applications of AI in the Development of a Sustainable Future provides a comprehensive, interdisciplinary perspective on the integration of AI technologies with renewable energy, smart infrastructure, environmental monitoring, and sustainable industrial practices. The book balances theory and practical application with chapters covering AI-driven solutions in energy optimisation, climate modelling, smart grids, precision agriculture, waste management, water resource management, and circular economy initiatives. Emerging AI techniques, including machine learning, deep learning, reinforcement learning, and predictive analytics, are examined for their potential to reduce environmental impact, enhance efficiency, and support data-driven decision-making in sustainability-focused domains. Case studies and real-world examples illustrate how AI applications are transforming industries and communities toward a greener, more resilient future. Key Features: -Provides interdisciplinary insights into connecting AI, environmental science, and sustainable development. -Real-world case studies demonstrating AI applications for renewable energy, smart infrastructure, and resource management. -Discusses ethical, social, and policy considerations in AI-enabled sustainability solutions. -Examines the impact of emerging AI techniques and their practical implications on sustainable practices.

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Pages: 250, Paperback, Bentham Science Publishers


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Merk Bentham Science Publishers
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  • 9798898813321
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