Artificial Intelligence-Driven Models for Environmental Management (eBook)
419 Seiten
Wiley (Verlag)
978-1-394-28254-8 (ISBN)
Step-by-step guidelines for the development of artificial neural network-based environmental pollution models
Artificial Intelligence-Driven Models for Environmental Management delves into the application of AI across a plethora of areas in environmental management, including climate forecasting, natural resource optimization, waste management, and biodiversity conservation. This book shows how AI can help in monitoring, predicting, and mitigating environmental impacts with tremendous accuracy and speed by leveraging machine learning, deep learning, and other data-driven models. The methodologies explored in this volume reflect a synthesis of computational intelligence, data science, and ecological expertise, underscoring how AI-driven systems have been making strides in managing and preserving our planet's natural resources.
The text is structured to guide readers through numerous AI models and their practical environmental management applications, showcasing theoretical foundations as well as case studies. This book also addresses the challenges and ethical considerations related to deploying AI in ecological contexts, underscoring the importance of transparency, inclusivity, and alignment with sustainability goals.
Sample topics discussed in Artificial Intelligence-Driven Models for Environmental Management include:
- Tools and methods for monitoring and predicting environmental pollutants faster and more accurately
- AI technology for the protection of water supplies from contamination to produce healthier foods
- Use of AI for the evaluation of the impacts of environmental pollution on human health
- AI and waste management technologies for sustainable agriculture and soil management
- The role of AI in environmental research and sustainability and key social and economic aspects of natural resource management through AI
Artificial Intelligence-Driven Models for Environmental Management is a timely, forward-thinking resource for a diverse readership, including researchers, policymakers, environmental scientists, and AI practitioners.
Shrikaant Kulkarni, Ph.D., is a Research Professor at Sanjivani University, Kopargaon, India, and an Adjunct Professor at Faculty of Business, Victorian Institute of Technology, Melbourne, Australia. Dr. Kulkarni has been a senior academic and researcher for more than four decades. He has published over 100 research papers, 100+ book chapters, and edited 50+ reference books.
Step-by-step guidelines for the development of artificial neural network-based environmental pollution models Artificial Intelligence-Driven Models for Environmental Management delves into the application of AI across a plethora of areas in environmental management, including climate forecasting, natural resource optimization, waste management, and biodiversity conservation. This book shows how AI can help in monitoring, predicting, and mitigating environmental impacts with tremendous accuracy and speed by leveraging machine learning, deep learning, and other data-driven models. The methodologies explored in this volume reflect a synthesis of computational intelligence, data science, and ecological expertise, underscoring how AI-driven systems have been making strides in managing and preserving our planet s natural resources. The text is structured to guide readers through numerous AI models and their practical environmental management applications, showcasing theoretical foundations as well as case studies. This book also addresses the challenges and ethical considerations related to deploying AI in ecological contexts, underscoring the importance of transparency, inclusivity, and alignment with sustainability goals. Sample topics discussed in Artificial Intelligence-Driven Models for Environmental Management include: Tools and methods for monitoring and predicting environmental pollutants faster and more accuratelyAI technology for the protection of water supplies from contamination to produce healthier foodsUse of AI for the evaluation of the impacts of environmental pollution on human healthAI and waste management technologies for sustainable agriculture and soil managementThe role of AI in environmental research and sustainability and key social and economic aspects of natural resource management through AI Artificial Intelligence-Driven Models for Environmental Management is a timely, forward-thinking resource for a diverse readership, including researchers, policymakers, environmental scientists, and AI practitioners.
| Erscheint lt. Verlag | 25.6.2025 |
|---|---|
| Sprache | englisch |
| Themenwelt | Naturwissenschaften ► Biologie ► Ökologie / Naturschutz |
| Schlagworte | AI-Driven Decision-Making • AI for Energy Efficiency • AI for Water Management • AI in Environmental Policy • AI in Forest Management • AI in Sustainability • AI in Waste Management • AI Models • Air Quality Prediction • Artificial Intelligence • Big Data Analytics • Biodiversity conservation • carbon footprint reduction • circular economy • Climate Change Mitigation • Climate modeling • Deep learning • disaster management • Ecosystem Preservation • Environmental Data • Environmental Impact Assessment • Environmental Management • Environmental monitoring • Environmental Risk Analysis • GIS and AI Integration • Greenhouse Gas Monitoring • Green Technology • IoT in Environment • machine learning • natural resource management • Precision Farming • predictive analytics • Remote Sensing • Renewable Energy Forecasting • Renewable Energy Optimization • smart cities • Smart Systems • sustainable agriculture • sustainable development |
| ISBN-10 | 1-394-28254-0 / 1394282540 |
| ISBN-13 | 978-1-394-28254-8 / 9781394282548 |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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