Data Analytics and Artificial Intelligence for Earth Resource Management
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Data Analytics and Artificial Intelligence for Earth Resource Management

Data Analytics and Artificial Intelligence for Earth Resource Management


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About the Book

Data 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.

Table of Contents:
Data analytics and artificial intelligence in Earth resource management Data analytics enabled by the Internet of Things and artificial intelligence for the management of Earth’s resources Data preprocessing techniques for earth resource management Artificial intelligence for sustainable stewardship of Earth resources Advancing earth resource management through AI enhanced early warning systems and crisis communication Artificial intelligence for analytical evaluation of landslide vulnerability Socioeconomic and environmental impacts analysis for climate resilient Earth resource management Data analytics for drought vulnerability under climate change scenarios Natural Language Processing for Earth resource management: a case of H2 Golden Retriever research Artificial intelligence in efficient management of water resources Groundwater potential zone evaluations for improving resource management with spatial analysis approach Future trends in computational data analytics and artificial intelligence for Earth resource management

About the Author :
Dr. Deepak Kumar is an academic researcher with a multidisciplinary background in Geospatial Sciences, Computational Sciences, Climate Change, and Sustainability. Currently a Research Scientist in the Atmospheric Sciences Group at Texas Tech University. Previously he served as Research Scientist in the Atmospheric Sciences Research Center at the State University of New York at Albany from August 2022 to November 2024. He's worked in the interdisciplinary research domain of the Urban-Climate-Energy nexus for policy making with humanities, social science, and technology perspectives. At Amity University, Delhi-NCR he was an Assistant Professor. His wide experience in the research development-cum-implementation pipeline comprising idea conceptualization, research design, data collection, processing, analysis, with result creation in the intersection areas of remote sensing and geoinformatics, environment, energy, climate change, urban weather and climate modelling, analysis, and visualization. He enjoys developing skills through conference appearances, outreach activities, training services and contributions to professional membership of scholarly associations. 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. 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.


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Product Details
  • ISBN-13: 9780443235955
  • Publisher: Elsevier - Health Sciences Division
  • Publisher Imprint: Elsevier - Health Sciences Division
  • Height: 235 mm
  • No of Pages: 308
  • Width: 191 mm
  • ISBN-10: 0443235953
  • Publisher Date: 06 Nov 2024
  • Binding: Paperback
  • Language: English
  • Weight: 640 gr


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