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Data Analytics and Artificial Intelligence for Earth Resource Management /

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...

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Enregistré dans:
Détails bibliographiques
Format: Électronique eBook
Langue:anglais
Publié: Amsterdam, Netherlands ; Oxford, United Kingdom ; Cambridge MA : Elsevier, [2025]
Sujets:
Natural resources > Management > Data processing.
Artificial intelligence.
Ressources naturelles > Gestion > Informatique.
Intelligence artificielle.
artificial intelligence.
Artificial intelligence
Natural resources > Management > Data processing
Accès en ligne:ScienceDirect
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Table des matières:
  • Front Cover
  • Data Analytics and Artificial Intelligence for Earth Resource Management
  • Copyright Page
  • Dedication
  • Contents
  • 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
  • 1 Data analytics and artificial intelligence in Earth resource management
  • 1.1 Introduction
  • 1.1.1 Data analytics and artificial intelligence
  • 1.1.2 Constituents of data analytics and artificial intelligence
  • 1.2 Significance of data analytics and artificial intelligence in Earth resource management
  • 1.2.1 Data analytics in Earth resource management
  • 1.2.2 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
  • 1.5.1 Key challenges faced by data analytics and artificial intelligence in Earth resource management
  • 1.5.2 Key opportunities for data analytics and artificial intelligence in Earth resource management
  • 1.6 Future of data analytics and artificial intelligence research for Earth resource management
  • 1.7 Conclusion
  • References
  • 2 Data analytics enabled by the Internet of Things and artificial intelligence for the management of Earth's resources
  • 2.1 Introduction
  • 2.2 Environmental factors in greenhouse operations
  • 2.2.1 Temperature
  • 2.2.2 Humidity
  • 2.2.3 Light
  • 2.2.4 Carbon dioxide levels
  • 2.2.5 Nutrient availability
  • 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
  • 3 Data preprocessing techniques for earth resource management
  • 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
  • 3.5.1 Sources of missing data
  • 3.5.2 Types of missing data
  • 3.5.3 Implications of missing data
  • 3.6 Approaches for managing missing data: data deletion strategies
  • 3.6.1 Pairwise deletion (or listwise deletion)
  • 3.6.2 Complete-case analysis
  • 3.6.3 Missingness indicator.
  • 3.6.4 Time-based deletion
  • 3.6.5 Variable deletion
  • 3.7 Outlier detection and treatment techniques
  • 3.7.1 Visual inspection
  • 3.7.2 Univariate and multivariate statistical methods
  • 3.7.3 Robust methods
  • 3.7.4 Machine learning algorithms
  • 3.7.5 Treatment strategies
  • 3.8 Data normalization and standardization
  • 3.8.1 Data normalization (min-max scaling)
  • 3.8.2 Data standardization (Z-score normalization)
  • 3.9 Feature selection and dimensionality reduction methods
  • 3.9.1 Feature selection
  • 3.9.2 Dimensionality reduction
  • 3.10 Handling categorical data: encoding techniques
  • 3.10.1 Encoding techniques for categorical variables
  • 3.10.2 Handling categorical data: feature engineering for categorical variables
  • 3.11 Data discretization and binning methods
  • 3.11.1 Equal-width discretization
  • 3.11.2 Equal-frequency discretization
  • 3.11.3 Custom discretization
  • 3.11.4 Entropy-based discretization
  • 3.11.5 Discretization based on clustering
  • 3.12 Balancing imbalanced datasets: oversampling, undersampling and hybrid methods
  • 3.12.1 Balancing imbalanced datasets: oversampling
  • 3.12.2 Balancing imbalanced datasets: undersampling
  • 3.12.3 Balancing imbalanced datasets: hybrid methods
  • 3.13 Case: studies as example
  • 3.14 Conclusion
  • References
  • 4 Artificial intelligence for sustainable stewardship of Earth resources
  • 4.1 Introduction
  • 4.1.1 Artificial intelligence in the Earth resource management
  • 4.1.2 Challenges of artificial intelligence in the Earth resource management
  • 4.1.3 Intelligent solutions for Earth resource management and sustainable management
  • 4.2 Literature review
  • 4.2.1 Purpose of the study
  • 4.3 Research methodology
  • 4.4 Result and analysis
  • 4.5 Conclusion
  • References
  • 5 Advancing earth resource management through AI enhanced early warning systems and crisis communication.
  • 5.1 Global imperatives amidst climate crisis
  • 5.1.1 Earth resource management
  • 5.1.2 Climate change and global impact
  • 5.2 Artificial intelligence and data analytics in Earth resource management
  • 5.2.1 Understanding the role of artificial intelligence and data analytics
  • 5.2.2 The advancements of artificial intelligence in reducing climate vulnerability
  • 5.2.3 Use of artificial intelligence in early warning systems
  • 5.3 The transformative potential of artificial intelligence and data analytics
  • 5.3.1 Responding to climate induced resource shortages and disasters
  • 5.3.2 Application of Artificial Intelligence in preventing loss of resources
  • 5.4 Effective communication and information dissemination with Artificial Intelligence
  • 5.4.1 Communication strategies in high risk regions
  • 5.4.2 Role of artificial intelligence in timely information dissemination
  • 5.4.3 Empowering communities with Artificial Intelligence-enhanced climate education
  • 5.4.4 Challenges: artificial intelligence and data analytics dangers and limitations
  • 5.5 Conclusion
  • 5.5.1 Artificial intelligence and Earth resource management: shaping artificial intelligence for social good
  • 5.5.2 Aligning technological advancement with sustainable and inclusive progress
  • References
  • 6 Artificial intelligence for analytical evaluation of landslide vulnerability
  • 6.1 Introduction
  • 6.2 Study area
  • 6.3 Datasets used and secondary data products
  • 6.4 Methodology
  • 6.4.1 Innovation
  • 6.4.2 Originality
  • 6.4.3 Model preparation
  • 6.5 Results and discussion
  • 6.5.1 Practical implications
  • 6.5.2 Examples and case studies
  • 6.6 Conclusion
  • References
  • 7 Socioeconomic and environmental impacts analysis for climate resilient Earth resource management
  • 7.1 Introduction
  • 7.1.1 Roots and tubers
  • 7.1.2 Taro and cassava
  • 7.1.3 Cassava
  • 7.1.4 Taro.
  • 7.1.5 Agroecological zoning
  • 7.2 Materials and methods
  • 7.3 Results
  • 7.4 Conclusions
  • References
  • 8 Data analytics for drought vulnerability under climate change scenarios
  • 8.1 Introduction
  • 8.1.1 Challenges and limitations in Earth resources management
  • 8.1.2 Effect of climate change on Earth resources management
  • 8.2 Role of artificial intelligence and data analytics in Earth resources management
  • 8.2.1 Artificial intelligence using artificial neural network method
  • 8.2.2 Data analytics using remote sensing and geographic information system
  • 8.3 Drought and its relationship with Earth resources management
  • 8.3.1 Understanding drought and its impact
  • 8.3.1.1 Classification of drought
  • 8.3.1.2 Impacts of drought
  • 8.3.2 Drought monitoring and management
  • 8.4 Drought risk assessment through remote sensing-geographic information system and artificial neural network
  • 8.4.1 Standard precipitation index
  • 8.4.2 Temperature condition index
  • 8.4.3 Vegetation condition index
  • 8.5 Future challenges and recommendations
  • 8.5.1 Emerging threats due to climate change
  • 8.5.2 Future recommendations
  • 8.6 Conclusion
  • 8.6.1 The way forward for drought management
  • References
  • 9 Natural Language Processing for Earth resource management: a case of H2 Golden Retriever research
  • 9.1 Introduction
  • 9.1.1 H2 production methods
  • 9.1.2 H2 ontology
  • 9.1.3 Contributions of study
  • 9.2 Materials and methods
  • 9.2.1 Data description
  • 9.2.2 Work study of the H2 Golden Retriever system
  • 9.2.3 System architecture of the H2 Golden Retriever system
  • 9.2.4 Component-based software detailing of the H2 Golden Retriever system
  • 9.3 Results and discussion
  • 9.3.1 The interactive user interface
  • 9.3.2 Ontology
  • 9.3.3 Cost modeling/causal decision diagram
  • 9.3.4 Topic modeling.

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