Artificial Intelligence-Driven Models for Environmental Management

• Record Nr.: UNINA9911018886203321
• Autore: Kulkarni Shrikaant
• Pubbl/distr/stampa: Newark : , : John Wiley & Sons, Incorporated, , 2025; ©2025
• ISBN: 9781394282548
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (349 pages)
• Disciplina: 363.7363028563
• Soggetti: Environmental monitoring - Technological innovations; Pollution - Measurement - Technological innovations; Artificial intelligence - Scientific applications
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Preface -- Part I Foundations of AI in Environmental Management -- Chapter 1 Application of AI in Environmental Sustainability -- 1.1 Introduction -- 1.1.1 Importance of AI in Addressing Environmental Challenges -- 1.2 AI Applications in Environmental Monitoring -- 1.2.1 Remote Sensing and Satellite Imaging -- 1.2.2 IoT Sensors and Data Collection -- 1.2.3 Predictive Analytics for Environmental Health -- 1.2.4 Real-Time Monitoring of Air and Water Quality -- 1.3 AI in Climate Change Mitigation -- 1.3.1 Predicting and Analyzing Climate Trends -- 1.3.2 AI-Driven Carbon Footprint Reduction Strategies -- 1.3.3 Renewable Energy Optimization Through AI -- 1.3.4 AI in Forest Conservation and Reforestation -- 1.4 AI in Resource Management -- 1.4.1 Sustainable Agriculture and AI-Assisted Precision Farming -- 1.4.2 AI in Water Resource Management and Conservation -- 1.4.3 Waste Management and Recycling Optimization -- 1.4.4 Circular Economy and Resource Efficiency -- 1.5 AI in Biodiversity Conservation -- 1.5.1 Wildlife Monitoring and Poaching Prevention -- 1.5.2 AI-Assisted Habitat Restoration -- 1.5.3 Species Identification and Population Tracking -- 1.5.4 Marine Ecosystem Management Through AI -- 1.6 AI in Sustainable Urban Planning -- 1.6.1 Smart Cities and Sustainable Infrastructure -- 1.6.2 ; AI in Reducing Urban Energy Consumption -- 1.6.3 Optimizing Urban Traffic for Reduced Emissions -- 1.6.4 AI-Enabled Green Building Design -- 1.7 Ethical and Governance Considerations -- 1.7.1 Ethical Implications of AI in Environmental Management -- 1.7.2 AI and Environmental Justice -- 1.7.3 Regulatory Frameworks for AI in Sustainability -- 1.7.4 Data Privacy and Security in Environmental AI Applications -- 1.7.5 Case Study -- 1.7.5.1 Background -- 1.7.5.2 Conclusion.; 1.8 Challenges and Future Prospects -- 1.8.1 Technological and Resource Limitations -- 1.8.2 Potential Risks and Unintended Consequences -- 1.8.3 AI's Role in Achieving Global Sustainability Goals -- 1.8.4 Future Directions in AI for Environmental Sustainability -- 1.9 Conclusion -- References -- Chapter 2 The Role of AI in Environmental Research and Sustainability -- 2.1 Introduction -- 2.1.1 Overview of AI in Environmental Research -- 2.1.2 Importance of AI in Sustainability Efforts -- 2.1.3 Scope and Objectives of the Study -- 2.2 AI Applications in Environmental Monitoring -- 2.2.1 Remote Sensing and Satellite Imaging -- 2.2.2 AI for Climate Modeling and Forecasting -- 2.2.3 Real-Time Environmental Data Collection -- 2.3 AI in Natural Resource Management -- 2.3.1 Optimizing Water and Energy Use -- 2.3.2 Smart Agriculture and Precision Farming -- 2.3.3 AI for Sustainable Fisheries and Forest Management -- 2.4 AI for Biodiversity and Ecosystem Conservation -- 2.4.1 AI-Powered Species Identification and Tracking -- 2.4.2 Monitoring and Protecting Endangered Species -- 2.4.3 Predictive Analytics in Habitat Restoration -- 2.5 AI in Urban Sustainability -- 2.5.1 AI in Smart Cities and Sustainable Urban Planning -- 2.5.2 Optimizing Transportation and Energy Grids -- 2.5.3 Waste Management and Recycling Innovations -- 2.6 Reducing Environmental Footprints with AI -- 2.6.1 AI for Energy Efficiency in Industries -- 2.6.2 AI and Carbon Emissions Reduction -- 2.6.3 AI in the Circular Economy and Waste Reduction -- 2.7 Ethical Considerations in AI-Driven Environmental Research -- 2.7.1 AI Ethics and Environmental Justice -- 2.7.2 Data Privacy and Security in Environmental Monitoring -- 2.7.3 Accountability and Transparency in AI Models -- 2.8 Case Study -- 2.8.1 Background -- 2.8.2 AI Implementation -- 2.8.3 Quantitative Analysis.; 2.8.4 Challenges and Opportunities -- 2.9 Conclusion -- References -- Chapter 3 AI and Environmental Data Science -- 3.1 Introduction -- 3.1.1 Background of AI in Environmental Science -- 3.1.2 Importance of Data Science in Environmental Studies -- 3.1.3 Objectives of the Study -- 3.2 Fundamentals of Artificial Intelligence -- 3.2.1 Overview of AI Techniques -- 3.2.2 Machine Learning vs. Traditional Approaches -- 3.2.3 Deep Learning and its Applications -- 3.3 Environmental Data Science -- 3.3.1 Definition and Scope -- 3.3.2 Types of Environmental Data -- 3.3.2.1 Satellite Imagery -- 3.3.2.2 Sensor Data -- 3.3.2.3 Climate and Weather Data -- 3.3.3 Data Collection and Management -- 3.4 AI Applications in Environmental Science -- 3.4.1 Predictive Modeling of Climate Change -- 3.4.2 Ecosystem Monitoring and Assessment -- 3.4.3 Biodiversity Conservation Efforts -- 3.4.4 Pollution Detection and Management -- 3.5 Case Studies -- 3.5.1 AI in Climate Resilience Planning -- 3.5.1.1 Case Study: City of San Francisco's Climate Resilience Strategy -- 3.5.2 Machine Learning for Wildlife Conservation -- 3.5.2.1 Case Study: African Wildlife Foundation's (AWF) Anti-poaching Initiative -- 3.5.3 Applications in Water Quality Monitoring -- 3.5.3.1 Case Study: The United Nations' "Water Quality and Ecosystems" Project -- 3.6 Challenges and Limitations -- 3.6.1 Data Quality and Availability -- 3.6.2 Interpretability of AI Models -- 3.6.3 Ethical Considerations -- 3.7 Case ; Study -- 3.7.1 Objective -- 3.7.2 Data Collection and AI Model Deployment -- 3.7.3 Results and Quantitative Analysis -- 3.7.4 Discussion -- 3.7.5 Challenges and Limitations -- 3.8 Future Directions -- 3.8.1 Emerging Trends in AI and Environmental Science -- 3.8.2 Integrating AI with Traditional Environmental Practices -- 3.8.3 Policy Implications and Recommendations -- 3.9 Conclusion -- References.; Part II AI in Natural Resource Management -- Chapter 4 Application of AI for Natural Source Management -- 4.1 Introduction -- 4.1.1 Importance of Natural Resource Management -- 4.1.2 Role of AI in Enhancing Resource Management -- 4.2 AI Technologies in NRM -- 4.2.1 Machine Learning Applications -- 4.2.2 Remote Sensing and Data Analysis -- 4.2.3 Predictive Analytics for Resource Forecasting -- 4.2.4 Geographic Information Systems (GIS) -- 4.3 Applications of AI in Specific Natural Resource Sectors -- 4.3.1 Water Resource Management -- 4.3.2 Forest Management and Conservation -- 4.3.3 Biodiversity Monitoring and Conservation -- 4.3.4 Agriculture and Land Use Optimization -- 4.4 Case Studies -- 4.4.1 AI in Water Quality Monitoring -- 4.4.2 Machine Learning for Forest Fire Prediction -- 4.4.3 AI-Driven Biodiversity Assessment -- 4.4.4 Smart Agriculture Solutions -- 4.5 Challenges and Limitations -- 4.5.1 Data Quality and Availability -- 4.5.2 Ethical Considerations -- 4.5.3 Implementation Barriers -- 4.5.4 Need for Interdisciplinary Collaboration -- 4.6 Future Directions -- 4.6.1 Innovations in AI Technologies -- 4.6.2 Enhancing Policy Frameworks -- 4.6.3 Public Engagement and Awareness -- 4.6.4 Integration of AI with Other Technologies -- 4.7 Case Study: Application of AI in NRM -- 4.7.1 Introduction -- 4.7.2 Objective -- 4.7.3 Approach -- 4.7.4 Results -- 4.7.4.1 Region A (Water Resource Management) -- 4.7.5 Discussion -- 4.7.6 Key Takeaways -- 4.7.7 Conclusion -- 4.7.8 Future Work -- References -- Chapter 5 Future Prospects of AI for Management of Natural Resources -- 5.1 Introduction -- 5.1.1 Importance of AI in Natural Resource Management -- 5.1.2 Objectives of the Study -- 5.2 Overview of AI Technologies -- 5.2.1 Machine Learning -- 5.2.2 Predictive Analytics -- 5.2.3 Real-Time Data Collection -- 5.2.4 Case Studies of AI Applications.; 5.3 AI in Water Management -- 5.3.1 Water Resource Allocation -- 5.3.2 Predicting Water Demand -- 5.3.3 Monitoring Water Quality -- 5.4 AI in Forestry -- 5.4.1 Forest Inventory and Monitoring -- 5.4.2 Predictive Modeling for Forest Health -- 5.4.3 Enhancing Reforestation Efforts -- 5.5 AI in Agriculture -- 5.5.1 Precision Agriculture -- 5.5.2 Crop Yield Prediction -- 5.5.3 Pest and Disease Management -- 5.6 AI in Biodiversity Conservation -- 5.6.1 Species Monitoring -- 5.6.2 Habitat Assessment -- 5.6.3 Predictive Conservation Planning -- 5.7 Challenges and Barriers to AI Implementation -- 5.7.1 Data Privacy Concerns -- 5.7.2 Ethical Considerations -- 5.7.3 The Digital Divide -- 5.8 Case Study -- 5.8.1 Objectives of the Case Study -- 5.8.2 Methodology -- 5.8.3 Quantitative Analysis -- 5.9 Conclusion -- References -- Part III AI Models for Climate Change Mitigation and Adaptation -- Chapter 6 AI in Climate Change Prediction -- 6.1 Introduction -- 6.1.1 Role of AI in Climate Science -- 6.1.2 How AI Enhances Climate Change Prediction -- 6.1.3 Real-World Applications of AI in Climate Prediction -- 6.1.4 AI and Climate Mitigation -- 6.1.5 Challenges and Limitations of AI in Climate Prediction -- 6.2 AI Technologies in Climate Prediction -- 6.2.1 Machine Learning for Climate Data Analysis -- 6.2.2 Deep Learning in Climate Models -- 6.2.3 AI-Powered Satellite Imagery Analysis -- 6.2.4 AI in Weather Forecasting and Extreme Event Prediction -- 6.3 AI Applications in ; Climate Science -- 6.3.1 Predicting Extreme Weather Events -- 6.3.2 Long-Term Climate Projections -- 6.3.3 AI in Ocean and Polar Ice Monitoring -- 6.3.4 AI in Air Quality and Pollution Forecasting -- 6.4 AI for Climate Mitigation and Adaptation -- 6.4.1 Optimizing Energy Consumption and Emission Reduction -- 6.4.2 AI in Renewable Energy Integration -- 6.4.3 AI in Smart Grids and Infrastructure.; 6.4.4 AI for Carbon Sequestration and Natural Resource Management.
• Sommario/riassunto: "This book provides tools and methods to monitor and predict environmental pollutants faster and more accurately. It covers different AI models and tools for achieving sustainable environmental development along as well as recent research directions for environmental issues. The book introduces novel intelligent techniques needed to address environmental pollution for global environmental health and puts forth insights on the next generation of intelligent pollution monitoring techniques. Topics include: Application of AI in Environmental Sustainability; The Role of AI in Environmental Research and Sustainability; The Living Environment and New Era of AI Education for a Sustainable Future; Managing Natural Resources Through Innovation: The Importance of Sustainable AI; AI-powered Soil Management; AI for Evaluation of the Impacts of Environmental Pollution on Human Health; Man-made Environmental Pollution with an Eye to Future Reduction using AI Network Techniques; AI Technology for Protection of Water Supplies from Contamination to Produce Healthy Foods; AI and Waste Management Technologies for Sustainable Agriculture; The Environmental AI Economy on Natural Resources Management; Environmental, Social and Economic Aspects of Natural Resource: AI Law and Policy Implications to Protect the Earth; AI in Healthy Natural Resource Management: Healthy Soils for Healthy Food Productions; Future Directions of AI for Management of Natural Resources"--