Artificial Intelligence-Driven Models for Environmental Management

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AI for Deforestation Monitoring
AI for Energy Conservation
AI for Energy Efficiency
AI for Environmental Education
AI for Environmental Justice Sustainable Supply Chains AI-Driven Resource Allocation
AI for Water Management
AI in Environmental Policy
AI in Forest Management
AI in Habitat Monitoring
AI in Hazardous Waste Management
AI in Land Management
AI in Plastic Waste Reduction
AI in Pollution Control
AI in Recycling
AI in Soil Management
AI in Sustainability
AI in Waste Management
AI in Wildlife Protection
AI Models
AI-Driven Decision-Making
AI-Driven Environmental Forecasting
AI-Enabled Climate Solutions
AI-Powered Sensors
Air Quality Prediction
Artificial Intelligence
Big Data Analytics
Biodiversity Conservation
Carbon Footprint Reduction
Category=PNC
Category=UYQ
Circular Economy
Clean Energy Systems
Clean Technology
Climate Adaptation Strategies
Climate Change Mitigation
Climate Modeling
Deep Learning
Disaster Management
Eco-Friendly Technologies
Ecosystem Preservation
Ecosystem Restoration
Energy Transition
Environmental Compliance
Environmental Data
Environmental Data Visualization
Environmental Economics
Environmental Ethics
Environmental Governance
Environmental Health
Environmental Impact Assessment
Environmental Innovation
Environmental Management
Environmental Monitoring
Environmental Risk Analysis
Environmental Robotics
Environmental Systems Modeling
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GIS and AI Integration
Green AI Applications
Green Technology
Greenhouse Gas Monitoring
Industrial Emissions Control
IoT in Environment
Land Use Management
Machine Learning
Natural Resource Management
Nature-Based Solutions
Noise Pollution Control
Precision Farming
Predictive Analytics
Remote Sensing
Renewable Energy Forecasting
Renewable Energy Optimization
Renewable Energy Systems
Resource Efficiency
Smart Cities
Smart Environmental Sensors
Smart Grids
Smart Systems
Sustainable Agriculture
Sustainable Development
Sustainable Development Goals (SDGs)
Sustainable Engineering
Sustainable Fisheries
Sustainable Infrastructure
Sustainable Mining
Sustainable Practices
Sustainable Transportation
Sustainable Urban Development
Urban Planning
Water Resource Optimization

Product details

  • ISBN 9781394282524
  • Weight: 826g
  • Publication Date: 24 Jun 2025
  • Publisher: John Wiley & Sons Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.