SUSTAINABLE DEVELOPMENT
Innovate. Sustain. Transform.
Save up to 30% on top Physical Sciences & Engineering titles!
Data Analytics and Artificial Intelligence for Earth Resource Management
- 1st Edition - November 6, 2024
- Editors: Deepak Kumar, Tavishi Tewary, Sulochana Shekhar
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 3 5 9 5 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 3 5 9 6 - 2
Data Analytics and Artificial Intelligence for Earth Resource Management offers a detailed look at the different ways data analytics and artificial intelligence can help organizat… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteData Analytics and Artificial Intelligence for Earth Resource Management offers a detailed look at the different ways data analytics and artificial intelligence can help organizations make better-informed decisions, improve operations, and minimize the negative impacts of resource extraction on the environment. The book explains several different ways data analytics and artificial intelligence can improve and support earth resource management. Predictive modeling can help organizations understand the impacts of different management decisions on earth resources, such as water availability, land use, and biodiversity.
Resource monitoring tracks the state of earth resources in real-time, identifying issues and opportunities for improvement. Providing managers with real-time data and analytics allows them to make more informed choices. Optimizing resource management decisions help to identify the most efficient and effective ways to allocate resources. Predictive maintenance allows organizations to anticipate when equipment might fail and take action to prevent it, reducing downtime and maintenance costs. Remote sensing with image processing and analysis can be used to extract information from satellite images and other remote sensing data, providing valuable information on land use, water resources, and other earth resources.
- Provides a comprehensive understanding of data analytics and artificial intelligence (AI) for earth resource management
- Includes real-world case studies and examples to demonstrate the practical applications of data analytics and AI in earth resource management
- Presents clear illustrations, diagrams, and pictures that make the content more understandable and engaging
Academicians, AI or Big-Data researchers, data scientists, data analysts, practitioners, and engineers in earth resource management, Also relevant to several industries and academic departments, including: Geography and Earth Sciences Departments, Computer Science and Engineering Departments, Civil and Infrastructure Engineering Departments, Environmental management and conservation, Mining and mineral exploration, Oil and gas industry, Business and Economics Departments, Mathematics and Statistics Departments
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- List of contributors
- Preface
- Chapter 1: Data analytics and artificial intelligence in Earth resource managements
- Chapter 2: Data analytics enabled by the Internet of Things and artificial intelligence for the management of Earth’s resources
- Chapter 3: Data preprocessing techniques for Earth resource management
- Chapter 4: Artificial intelligence for sustainable stewardship of Earth resources
- Chapter 5: Artificial intelligence and data analytics for early warning systems and Earth resource management
- Chapter 6: Artificial intelligence for analytical evaluation of landslide vulnerability
- Chapter 7: Socioeconomic and environmental impacts analysis for climate resilient Earth resource management
- Chapter 8: Data analytics for drought vulnerability under climate change scenarios
- Chapter 9: Natural Language Processing for Earth resource management: a case of H2 Golden Retriever research
- Chapter 10: Artificial intelligence in efficient management of water resources
- Chapter 11: Groundwater potential zone evaluations for improving resource management with spatial analysis approach
- Chapter 12: Future trends in computational data analytics and artificial intelligence for Earth resource management
- Acknowledgments
- Chapter 1. Data analytics and artificial intelligence in Earth resource management
- Abstract
- 1.1 Introduction
- 1.2 Significance of data analytics and artificial intelligence in Earth resource management
- 1.3 Benefits of data analytics and artificial intelligence in Earth resource management
- 1.4 Approaches of data analytics and artificial intelligence in Earth resource management
- 1.5 Challenges and opportunities
- 1.6 Future of data analytics and artificial intelligence research for Earth resource management
- 1.7 Conclusion
- References
- Chapter 2. Data analytics enabled by the Internet of Things and artificial intelligence for the management of Earth’s resources
- Abstract
- 2.1 Introduction
- 2.2 Environmental factors in greenhouse operations
- 2.3 Internet of Things applications in greenhouse environments
- 2.4 Benefits of Internet of Things in greenhouse operations
- 2.5 Challenges and considerations in implementing Internet of Things
- 2.6 Sustainable growth through Internet of Things in greenhouse agriculture
- 2.7 Real-world examples and case studies
- 2.8 Future trends and potential developments
- 2.9 Conclusion
- References
- Chapter 3. Data preprocessing techniques for earth resource management
- Abstract
- 3.1 Introduction
- 3.2 Key aspects and benefits of data preprocessing
- 3.3 Opportunities for data preprocessing Earth resource management
- 3.4 Data preprocessing techniques for Earth resource management
- 3.5 Missing data in research and data analysis
- 3.6 Approaches for managing missing data: data deletion strategies
- 3.7 Outlier detection and treatment techniques
- 3.8 Data normalization and standardization
- 3.9 Feature selection and dimensionality reduction methods
- 3.10 Handling categorical data: encoding techniques
- 3.11 Data discretization and binning methods
- 3.12 Balancing imbalanced datasets: oversampling, undersampling and hybrid methods
- 3.13 Case: studies as example
- 3.14 Conclusion
- References
- Chapter 4. Artificial intelligence for sustainable stewardship of Earth resources
- Abstract
- 4.1 Introduction
- 4.2 Literature review
- 4.3 Research methodology
- 4.4 Result and analysis
- 4.5 Conclusion
- References
- Chapter 5. Advancing earth resource management through AI enhanced early warning systems and crisis communication
- Abstract
- 5.1 Global imperatives amidst climate crisis
- 5.2 Artificial intelligence and data analytics in Earth resource management
- 5.3 The transformative potential of artificial intelligence and data analytics
- 5.4 Effective communication and information dissemination with Artificial Intelligence
- 5.5 Conclusion
- References
- Chapter 6. Artificial intelligence for analytical evaluation of landslide vulnerability
- Abstract
- 6.1 Introduction
- 6.2 Study area
- 6.3 Datasets used and secondary data products
- 6.4 Methodology
- 6.5 Results and discussion
- 6.6 Conclusion
- References
- Chapter 7. Socioeconomic and environmental impacts analysis for climate resilient Earth resource management
- Abstract
- 7.1 Introduction
- 7.2 Materials and methods
- 7.3 Results
- 7.4 Conclusions
- References
- Chapter 8. Data analytics for drought vulnerability under climate change scenarios
- Abstract
- 8.1 Introduction
- 8.2 Role of artificial intelligence and data analytics in Earth resources management
- 8.3 Drought and its relationship with Earth resources management
- 8.4 Drought risk assessment through remote sensing-geographic information system and artificial neural network
- 8.5 Future challenges and recommendations
- 8.6 Conclusion
- References
- Chapter 9. Natural Language Processing for Earth resource management: a case of H2 Golden Retriever research
- Abstract
- 9.1 Introduction
- 9.2 Materials and methods
- 9.3 Results and discussion
- 9.4 Conclusion and future work
- Acknowledgment
- References
- Chapter 10. Artificial intelligence in efficient management of water resources
- Abstract
- 10.1 Introduction
- 10.2 Artificial intelligence in tackling water-related disasters
- 10.3 Artificial intelligence and modeling framework
- 10.4 Artificial intelligence for water quality assessment
- 10.5 Summary and conclusion
- References
- Chapter 11. Groundwater potential zone evaluations for improving resource management with spatial analysis approach
- Abstract
- 11.1 Introduction
- 11.2 Materials and methods
- 11.3 Preparation of thematic layers
- 11.4 Discussions
- 11.5 Conclusion
- References
- Further reading
- Chapter 12. Future trends in computational data analytics and artificial intelligence for Earth resource management
- Abstract
- 12.1 Introduction
- 12.2 Remote sensing and image analysis
- 12.3 Natural resource exploration and extraction
- 12.4 Environmental monitoring and conservation
- 12.5 Sustainable agriculture
- 12.6 Energy management and efficiency
- 12.7 Disaster management and emergency response
- 12.8 Outcomes, discussion, and conclusion
- Acknowledgments
- References
- Index
- No. of pages: 325
- Language: English
- Edition: 1
- Published: November 6, 2024
- Imprint: Elsevier
- Paperback ISBN: 9780443235955
- eBook ISBN: 9780443235962
DK
Deepak Kumar
Dr. Deepak Kumar is an early career academic research professional with a multidisciplinary academic background in geospatial sciences, computational sciences, climate change, and sustainability with over 10 years of experience. Dr Kumar has been working in interdisciplinary research towards the urban–climate–energy nexus to advance policies considering humanities, social science, and technology perspectives. Presently, he is working as a Research Scientist at the Center of Excellence in Weather and Climate Analytics, Atmospheric Science Research Center (ASRC), State University of New York at Albany, New York (United States). He has been also associated as a full-time Assistant Professor with Amity University, Uttar Pradesh, Noida (India). He has been revisiting a wide range of issues associated with traditional research activities in the intersection area of remote sensing and geoinformatics, environment, energy, climate change, urban weather and climate modelling, analysis, and visualization. He has wide experience in research pipeline embracing idea conceptualization, research design, data collection, processing, analysis and result generation. He completed two government-sponsored research projects sponsored as a sole principal investigator, supervised cross-functional research group of 4 PhD and 29 postgraduate students, and conducted 15 academic, research, and industrial visits for undergraduate students. He has published 50+ research articles in a high-impact web of science/Scopus-indexed international journals by Elsevier, Springer, Taylor & Francis, Emerald, SAGE, and Wiley publications. He has reviewed more than 220+ research articles as an ad hoc/adjunct/invited reviewer or editorial board member for high-impact factor journals published by IEEE, Elsevier, Springer Nature, Taylor & Francis Group, Wiley, SAGE, Emerald, and MDPI. He has authored/edited/co-edited 9 books with Elsevier, Springer, Taylor & Francis, Emerald, SAGE, and Wiley publications. He has been dedicated to finding solutions to complex problems in the areas of climate change, urban planning, energy, the environment, and sustainability (SDGs 7, 10, 11, and 13).
Affiliations and expertise
Research Scientist Center of Excellence in Weather and Climate Analytics, Atmospheric Sciences Research Center (ASRC) and Amity Institute of Geoinformatics and Remote Sensing (AIGIRS)University, Albany, State University of New York, NY, USATT
Tavishi Tewary
Dr. Tavishi Tewary has over 16 years of experience in policy research and trade impact assessment. She has published various research papers in international journals of high repute. She has academic experience teaching postgraduate business school students. She also has experience in providing scientific leadership to high-profile strategic sustainability and conservation initiatives with several years of research experience. She managed cross-sectoral and interdisciplinary teams of professionals to deliver on complex research projects. Currently, she is working as an Assistant Professor at FORE School of Management, New Delhi (India). Her area of interest covers international trade analysis, sustainability, international economics, socioeconomic development, energy, circular economy, and Industry 4.0. She has presented several research papers at international and national conferences on these topics. She has authored a book on circular economy with a publisher of international repute. She has also conducted FDPs on data analysis using SPSS, PLS-SEM, and EViews. Also, she has experience in providing scientific leadership to high-profile strategic sustainability and conservation initiatives with several years of research experience.
Affiliations and expertise
Assistant ProfessorJaipuria Institute of Management, NoidaUttar PradeshIndiaSS
Sulochana Shekhar
Dr. Sulochana Shekhar is the Dean of the School of Earth Sciences at the Central University of Tamil Nadu, Thiruvarur, India. Her research interests include urban geography and the application of remote sensing and geospatial techniques in urban environments. She has worked on cellular automata-based urban growth models and urban sprawl assessment using entropy for her doctoral research. Object-based image analysis was the main theme of her postdoctoral research at ITC, the Netherlands. She has previously worked on projects involving spatial decision support systems for slums (UCL, UK) and extraction of slums and urban green space using object-based image analysis (UTAS, Australia). She has completed major funded projects on housing the urban poor (HUDCO) and the environmental impact of urbanization (UGC Major) using geospatial techniques. She has collaborated with Cambridge University, UK and completed the UKIERI research project on the interactive spatial decision support system for managing public health.
Affiliations and expertise
Professor and Dean, Central University of Tamil Nadu (CUTN) School of Earth Sciences, CUTN Bridge, Neelakudi, Tamil Nadu, IndiaRead Data Analytics and Artificial Intelligence for Earth Resource Management on ScienceDirect