Artificial Intelligence-Driven Models for Environmental Management -

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Résumé :

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....

Sommaire:

List of Contributors xxi

Preface xxiii

Part I Foundations of AI in Environmental Management 1

1 Application of AI in Environmental Sustainability 3
Pawan Whig, Shashi Kant Gupta, Rahul Reddy Nadikattu, and Pavika Sharma

1.1 Introduction 3

1.2 AI Applications in Environmental Monitoring 6

1.3 AI in Climate Change Mitigation 9

1.4 AI in Resource Management 13

1.5 AI in Biodiversity Conservation 17

1.6 AI in Sustainable Urban Planning 21

1.7 Ethical and Governance Considerations 25

1.8 Challenges and Future Prospects 33

1.9 Conclusion 38

References 38

2 The Role of AI in Environmental Research and Sustainability 43
Iti Batra, Seema Nath Jain, Nikhitha Yathiraju, and Kavita Mittal

2.1 Introduction 43

2.2 AI Applications in Environmental Monitoring 46

2.3 AI in Natural Resource Management 50

2.4 AI for Biodiversity and Ecosystem Conservation 53

2.5 AI in Urban Sustainability 56

2.6 Reducing Environmental Footprints with AI 59

2.7 Ethical Considerations in AI-Driven Environmental Research 62

2.8 Case Study 65

2.9 Conclusion 67

References 68

3 AI and Environmental Data Science 71
Ashima Bhatnagar Bhatia, Meghna Sharma, and Bhupesh Bhatia

3.1 Introduction 71

3.2 Fundamentals of Artificial Intelligence 74

3.3 Environmental Data Science 76

3.4 AI Applications in Environmental Science 80

3.5 Case Studies 83

3.6 Challenges and Limitations 86

3.7 Case Study 88

3.8 Future Directions 91

3.9 Conclusion 94

References 95

Part II AI in Natural Resource Management 99

4 Application of AI for Natural Source Management 101
Pawan Whig, Rahul Reddy Nadikattu, Shashi Kant Gupta, and Shrikaant Kulkarni

4.1 Introduction 101

4.2 AI Technologies in NRM 103

4.3 Applications of AI in Specific Natural Resource Sectors 106

4.4 Case Studies 108

4.5 Challenges and Limitations 110

4.6 Future Directions 112

4.7 Case Study: Application of AI in NRM 114

References 117

5 Future Prospects of AI for Management of Natural Resources 121
Meghna Sharma, Ashima Bhatnagar Bhatia, and Bhupesh Bhatia

5.1 Introduction 121

5.2 Overview of AI Technologies 123

5.3 AI in Water Management 125

5.4 AI in Forestry 127

5.5 AI in Agriculture 129

5.6 AI in Biodiversity Conservation 131

5.7 Challenges and Barriers to AI Implementation 134

5.8 Case Study 136

5.9 Conclusion 139

References 139

Part III AI Models for Climate Change Mitigation and Adaptation 143

6 AI in Climate Change Prediction 145
Seema Sharma, Anupriya Jain, Sachin Sharma, and Sonia Duggal

6.1 Introduction 145

6.2 AI Technologies in Climate Prediction 148

6.3 AI Applications in Climate Science 150

6.4 AI for Climate Mitigation and Adaptation 152

6.5 Case Studies 155

6.6 Case Study: IBM's Green Horizon Project for Air Quality Prediction 156

References 159

7 AI-Driven Environmental Real-Time Monitoring, and Screening 163
Kavita Mittal, Rahul Reddy Nadikattu, Pawan Whig, and Iti Batra

7.1 Introduction 163

7.2 Understanding AI in Environmental Monitoring 166

7.3 Applications of AI in Real-Time Environmental Monitoring 168

7.4 AI Techniques for Screening Environmental Data 171

7.5 Case Studies of AI-Driven Environmental Monitoring 174

7.6 Challenges in Implementing AI for Environmental Moni...