Computational Intelligent Data Analysis for Sustainable Development

Book description

Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development present

Table of contents

  1. Front Cover (1/2)
  2. Front Cover (2/2)
  3. Contents
  4. Acknowledgments
  5. About the Editors
  6. List of Contributors
  7. Chapter 1. Computational Intelligent Data Analysis for Sustainable Development (1/6)
  8. Chapter 1. Computational Intelligent Data Analysis for Sustainable Development (2/6)
  9. Chapter 1. Computational Intelligent Data Analysis for Sustainable Development (3/6)
  10. Chapter 1. Computational Intelligent Data Analysis for Sustainable Development (4/6)
  11. Chapter 1. Computational Intelligent Data Analysis for Sustainable Development (5/6)
  12. Chapter 1. Computational Intelligent Data Analysis for Sustainable Development (6/6)
  13. Chapter 2. Tracing Embodied CO2 in Trade Using High-Resolution Input–Output Tables (1/6)
  14. Chapter 2. Tracing Embodied CO2 in Trade Using High-Resolution Input–Output Tables (2/6)
  15. Chapter 2. Tracing Embodied CO2 in Trade Using High-Resolution Input–Output Tables (3/6)
  16. Chapter 2. Tracing Embodied CO2 in Trade Using High-Resolution Input–Output Tables (4/6)
  17. Chapter 2. Tracing Embodied CO2 in Trade Using High-Resolution Input–Output Tables (5/6)
  18. Chapter 2. Tracing Embodied CO2 in Trade Using High-Resolution Input–Output Tables (6/6)
  19. Chapter 3. Aggregation Effects in Carbon Footprint Accounting Using Multi-Region Input–Output Analysis (1/6)
  20. Chapter 3. Aggregation Effects in Carbon Footprint Accounting Using Multi-Region Input–Output Analysis (2/6)
  21. Chapter 3. Aggregation Effects in Carbon Footprint Accounting Using Multi-Region Input–Output Analysis (3/6)
  22. Chapter 3. Aggregation Effects in Carbon Footprint Accounting Using Multi-Region Input–Output Analysis (4/6)
  23. Chapter 3. Aggregation Effects in Carbon Footprint Accounting Using Multi-Region Input–Output Analysis (5/6)
  24. Chapter 3. Aggregation Effects in Carbon Footprint Accounting Using Multi-Region Input–Output Analysis (6/6)
  25. Chapter 4. Climate Informatics (1/10)
  26. Chapter 4. Climate Informatics (2/10)
  27. Chapter 4. Climate Informatics (3/10)
  28. Chapter 4. Climate Informatics (4/10)
  29. Chapter 4. Climate Informatics (5/10)
  30. Chapter 4. Climate Informatics (6/10)
  31. Chapter 4. Climate Informatics (7/10)
  32. Chapter 4. Climate Informatics (8/10)
  33. Chapter 4. Climate Informatics (9/10)
  34. Chapter 4. Climate Informatics (10/10)
  35. Chapter 5. Computational Data Sciences for Actionable Insights on Climate Extremes and Uncertainty (1/7)
  36. Chapter 5. Computational Data Sciences for Actionable Insights on Climate Extremes and Uncertainty (2/7)
  37. Chapter 5. Computational Data Sciences for Actionable Insights on Climate Extremes and Uncertainty (3/7)
  38. Chapter 5. Computational Data Sciences for Actionable Insights on Climate Extremes and Uncertainty (4/7)
  39. Chapter 5. Computational Data Sciences for Actionable Insights on Climate Extremes and Uncertainty (5/7)
  40. Chapter 5. Computational Data Sciences for Actionable Insights on Climate Extremes and Uncertainty (6/7)
  41. Chapter 5. Computational Data Sciences for Actionable Insights on Climate Extremes and Uncertainty (7/7)
  42. Chapter 6. Mathematical Programming Applications to Land Conservation and Environmental Quality (1/5)
  43. Chapter 6. Mathematical Programming Applications to Land Conservation and Environmental Quality (2/5)
  44. Chapter 6. Mathematical Programming Applications to Land Conservation and Environmental Quality (3/5)
  45. Chapter 6. Mathematical Programming Applications to Land Conservation and Environmental Quality (4/5)
  46. Chapter 6. Mathematical Programming Applications to Land Conservation and Environmental Quality (5/5)
  47. Chapter 7. Data Analysis Challenges in the Future Energy Domain (1/13)
  48. Chapter 7. Data Analysis Challenges in the Future Energy Domain (2/13)
  49. Chapter 7. Data Analysis Challenges in the Future Energy Domain (3/13)
  50. Chapter 7. Data Analysis Challenges in the Future Energy Domain (4/13)
  51. Chapter 7. Data Analysis Challenges in the Future Energy Domain (5/13)
  52. Chapter 7. Data Analysis Challenges in the Future Energy Domain (6/13)
  53. Chapter 7. Data Analysis Challenges in the Future Energy Domain (7/13)
  54. Chapter 7. Data Analysis Challenges in the Future Energy Domain (8/13)
  55. Chapter 7. Data Analysis Challenges in the Future Energy Domain (9/13)
  56. Chapter 7. Data Analysis Challenges in the Future Energy Domain (10/13)
  57. Chapter 7. Data Analysis Challenges in the Future Energy Domain (11/13)
  58. Chapter 7. Data Analysis Challenges in the Future Energy Domain (12/13)
  59. Chapter 7. Data Analysis Challenges in the Future Energy Domain (13/13)
  60. Chapter 8. Electricity Supply without Fossil Fuels (1/6)
  61. Chapter 8. Electricity Supply without Fossil Fuels (2/6)
  62. Chapter 8. Electricity Supply without Fossil Fuels (3/6)
  63. Chapter 8. Electricity Supply without Fossil Fuels (4/6)
  64. Chapter 8. Electricity Supply without Fossil Fuels (5/6)
  65. Chapter 8. Electricity Supply without Fossil Fuels (6/6)
  66. Chapter 9. Data Analysis for Real-Time Identification of Grid Disruptions (1/6)
  67. Chapter 9. Data Analysis for Real-Time Identification of Grid Disruptions (2/6)
  68. Chapter 9. Data Analysis for Real-Time Identification of Grid Disruptions (3/6)
  69. Chapter 9. Data Analysis for Real-Time Identification of Grid Disruptions (4/6)
  70. Chapter 9. Data Analysis for Real-Time Identification of Grid Disruptions (5/6)
  71. Chapter 9. Data Analysis for Real-Time Identification of Grid Disruptions (6/6)
  72. Chapter 10. Statistical Approaches for Wind Resource Assessment (1/6)
  73. Chapter 10. Statistical Approaches for Wind Resource Assessment (2/6)
  74. Chapter 10. Statistical Approaches for Wind Resource Assessment (3/6)
  75. Chapter 10. Statistical Approaches for Wind Resource Assessment (4/6)
  76. Chapter 10. Statistical Approaches for Wind Resource Assessment (5/6)
  77. Chapter 10. Statistical Approaches for Wind Resource Assessment (6/6)
  78. Chapter 11. Spatio-Temporal Correlations in Criminal Offense Records (1/6)
  79. Chapter 11. Spatio-Temporal Correlations in Criminal Offense Records (2/6)
  80. Chapter 11. Spatio-Temporal Correlations in Criminal Offense Records (3/6)
  81. Chapter 11. Spatio-Temporal Correlations in Criminal Offense Records (4/6)
  82. Chapter 11. Spatio-Temporal Correlations in Criminal Offense Records (5/6)
  83. Chapter 11. Spatio-Temporal Correlations in Criminal Offense Records (6/6)
  84. Chapter 12. Constraint and Optimization Techniques for Supporting Policy Making (1/5)
  85. Chapter 12. Constraint and Optimization Techniques for Supporting Policy Making (2/5)
  86. Chapter 12. Constraint and Optimization Techniques for Supporting Policy Making (3/5)
  87. Chapter 12. Constraint and Optimization Techniques for Supporting Policy Making (4/5)
  88. Chapter 12. Constraint and Optimization Techniques for Supporting Policy Making (5/5)
  89. Index (1/8)
  90. Index (2/8)
  91. Index (3/8)
  92. Index (4/8)
  93. Index (5/8)
  94. Index (6/8)
  95. Index (7/8)
  96. Index (8/8)
  97. Back Cover

Product information

  • Title: Computational Intelligent Data Analysis for Sustainable Development
  • Author(s): Ting Yu, Nitesh Chawla, Simeon Simoff
  • Release date: April 2016
  • Publisher(s): Chapman and Hall/CRC
  • ISBN: 9781439895955