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Machine learning and Internet of thi...

Arora, Manmeet Kaur.

Machine learning and Internet of things in fire ecology

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning and Internet of things in fire ecologyChristian Kaunert, Rishabha Malviya, Bhupinder Singh, Sahil Lal, Manmeet Kaur Arora, editors.
other author:	Arora, Manmeet Kaur.
Published:	Hershey, Pennsylvania :IGI Global Scientific Publishing,2025.
Description:	1 online resource (xvi, 374 p.)
Subject:	Forest firesPrevention and control
Online resource:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-7565-5
ISBN:	9798369375679$q(ebook)

Machine learning and Internet of things in fire ecology
Machine learning and Internet of things in fire ecology[electronic resource] /Christian Kaunert, Rishabha Malviya, Bhupinder Singh, Sahil Lal, Manmeet Kaur Arora, editors. - Hershey, Pennsylvania :IGI Global Scientific Publishing,2025. - 1 online resource (xvi, 374 p.) - Advances in environmental engineering and green technologies (AEEGT) book series. - Advances in environmental engineering and green technologies (AEEGT) book series..

Includes bibliographical references and index.

Preface -- Chapter 1. Addressing the Impacts of Annual Wildfires on Biodiversity and Ecosystem Services: A Review of Global Efforts -- Chapter 2. Advancing Artificial Intelligence for Wildfire Prediction and Control: New Edges in Environmental Safety and Fire Ecology -- Chapter 3. AI and ML Applications in Wildlife Conservation and Forest Management: A Comprehensive Review -- Chapter 4. Artificial Intelligence and Machine Learning Technological Tools in Enhancing Efficiency in Wildlife and Forest Conservation -- Chapter 5. Discovering Advances in Using Artificial Techniques (AI) Techniques to Understand and Manage Wildfires -- Chapter 6. Emerging Trends of IoT in Conservation of Biodiversity: A Futuristic Approach -- Chapter 7. Integrating Machine Learning Into Wildlife and Forest Conservation: Ethical Considerations and Sustainable Practices -- Chapter 8. Integrating UAV-IoT Networks for Proactive Wildfire Detection: Innovations and Future Directions -- Chapter 9. IoT Networks and ML in Fire Ecology Innovations and Impacts -- Chapter 10. Modernizing Wildfire Management Through Deep Learning and IoT in Fire Ecology -- Chapter 11. Prospects and Challenges of Artificial Intelligence in Forest Protection -- Chapter 12. Role of Machine Learning and Deep Learning in Forest Management Through Data Analytics -- Chapter 13. Role of UAV-IoT Networks in Future Wildfire Detection -- Chapter 14. Wildlife and Forest Resource Management With Artificial Intelligence -- Compilation of References -- About the Contributors -- Index.

"The destruction of millions of acres of forest land through wildfires is a global cause of concern. Artificial intelligence (AI), transformative in nature, has the potential to transcend and significantly mitigate risk factors of wildfires. AI-driven monitoring systems can detect early signs of wildfire activity, allowing for faster, more targeted responses that can minimize damage and save lives. Machine Learning and Internet of Things in Fire Ecology elucidates andexplores the interface of fire ecology with AI, machine learning, and internet of things, as these technologies emerged as a pivotal domain with transformative potential. It will assist environmental-related industries in understanding theparaphernalia and dynamics of the fire ecology ecosystem. Covering topics such as AI, unmanned aerial vehicles (UAVs), and wildlife conservation, this book is an excellent resource for government officials, ecologists, academicians, policymakers, researchers, environmental specialists, industry experts, graduate and postgraduate students, and more."--

Mode of access: World Wide Web.

ISBN: 9798369375679$q(ebook)Subjects--Topical Terms:

995244
Forest fires
--Prevention and controlSubjects--Index Terms:

Artificial Intelligence (AI)Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: SD421.45.D37 / M33 2025eb

Dewey Class. No.: 634.9/6180285

Machine learning and Internet of things in fire ecology
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300 $a 1 online resource (xvi, 374 p.)
490 1 $a Advances in environmental engineering and green technologies (AEEGT) book series
504 $a Includes bibliographical references and index.
505 0 $a Preface -- Chapter 1. Addressing the Impacts of Annual Wildfires on Biodiversity and Ecosystem Services: A Review of Global Efforts -- Chapter 2. Advancing Artificial Intelligence for Wildfire Prediction and Control: New Edges in Environmental Safety and Fire Ecology -- Chapter 3. AI and ML Applications in Wildlife Conservation and Forest Management: A Comprehensive Review -- Chapter 4. Artificial Intelligence and Machine Learning Technological Tools in Enhancing Efficiency in Wildlife and Forest Conservation -- Chapter 5. Discovering Advances in Using Artificial Techniques (AI) Techniques to Understand and Manage Wildfires -- Chapter 6. Emerging Trends of IoT in Conservation of Biodiversity: A Futuristic Approach -- Chapter 7. Integrating Machine Learning Into Wildlife and Forest Conservation: Ethical Considerations and Sustainable Practices -- Chapter 8. Integrating UAV-IoT Networks for Proactive Wildfire Detection: Innovations and Future Directions -- Chapter 9. IoT Networks and ML in Fire Ecology Innovations and Impacts -- Chapter 10. Modernizing Wildfire Management Through Deep Learning and IoT in Fire Ecology -- Chapter 11. Prospects and Challenges of Artificial Intelligence in Forest Protection -- Chapter 12. Role of Machine Learning and Deep Learning in Forest Management Through Data Analytics -- Chapter 13. Role of UAV-IoT Networks in Future Wildfire Detection -- Chapter 14. Wildlife and Forest Resource Management With Artificial Intelligence -- Compilation of References -- About the Contributors -- Index.
520 3 $a "The destruction of millions of acres of forest land through wildfires is a global cause of concern. Artificial intelligence (AI), transformative in nature, has the potential to transcend and significantly mitigate risk factors of wildfires. AI-driven monitoring systems can detect early signs of wildfire activity, allowing for faster, more targeted responses that can minimize damage and save lives. Machine Learning and Internet of Things in Fire Ecology elucidates andexplores the interface of fire ecology with AI, machine learning, and internet of things, as these technologies emerged as a pivotal domain with transformative potential. It will assist environmental-related industries in understanding theparaphernalia and dynamics of the fire ecology ecosystem. Covering topics such as AI, unmanned aerial vehicles (UAVs), and wildlife conservation, this book is an excellent resource for government officials, ecologists, academicians, policymakers, researchers, environmental specialists, industry experts, graduate and postgraduate students, and more."-- $c Provided by publisher.
538 $a Mode of access: World Wide Web.
650 0 $a Forest fires $x Prevention and control $x Data processing. $3 995244
650 0 $a Fire ecology $x Data processing. $3 995245
650 0 $a Forest fires $x Prevention and control $x Technological innovations. $3 995246
650 0 $a Forests and forestry $x Fire management. $3 995247
650 0 $a Forest management $x Technological innovations. $3 995248
653 $a Artificial Intelligence (AI)
653 $a Biodiversity.
653 $a Data Analytics.
653 $a Environmental Safety.
653 $a Fire Ecology.
653 $a Forest Management.
653 $a Internet of Things (IoT)
653 $a Machine Learning (ML)
653 $a Unmanned Aerial Vehicles (UAVs)
653 $a Wildfire Detection.
653 $a Wildfire Prediction.
653 $a Wildlife Conservation.
655 4 $a Electronic books. $2 local. $3 214472
700 1 $a Arora, Manmeet Kaur. $3 995241
700 1 $a Lal, Sahil. $3 995242
700 1 $a Singh, Bhupinder. $3 995243
700 1 $a Malviya, Rishabha. $3 951349
700 1 $a Kaunert, Christian. $3 644590
710 2 $a IGI Global. $3 529832
776 0 8 $i Print version: $z 9798369375655
830 0 $a Advances in environmental engineering and green technologies (AEEGT) book series. $3 899881
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-7565-5