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...
Furkejuvvon:
| Materiálatiipa: | Elektrovnnalaš E-girji |
|---|---|
| Giella: | eaŋgalasgiella |
| Almmustuhtton: |
Amsterdam, Netherlands ; Oxford, United Kingdom ; Cambridge MA :
Elsevier,
[2025]
|
| Fáttát: | |
| Liŋkkat: | ScienceDirect |
| Fáddágilkorat: |
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| 245 | 0 | 0 | |a Data Analytics and Artificial Intelligence for Earth Resource Management / |c edited by Deepak Kumar, Tavishi Tewary, Sulochana Shekhar. |
| 264 | 1 | |a Amsterdam, Netherlands ; |a Oxford, United Kingdom ; |a Cambridge MA : |b Elsevier, |c [2025] | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on December 17, 2024). | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Natural resources |x Management |x Data processing. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 6 | |a Ressources naturelles |0 (CaQQLa)201-0186248 |x Gestion |0 (CaQQLa)201-0186248 |x Informatique. |0 (CaQQLa)201-0380011 | |
| 650 | 6 | |a Intelligence artificielle. |0 (CaQQLa)201-0008626 | |
| 650 | 7 | |a artificial intelligence. |2 aat |0 (CStmoGRI)aat300251574 | |
| 650 | 7 | |a Artificial intelligence |2 fast |0 (OCoLC)fst00817247 | |
| 650 | 7 | |a Natural resources |x Management |x Data processing |2 fast |0 (OCoLC)fst01034441 | |
| 776 | 0 | 8 | |i Print version: |z 0443235953 |z 9780443235955 |w (OCoLC)1419059660 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443235955 |