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Acknowledgments=xi

About the Editors=xiii

List of Contributors=xv

CHAPTER 1. Computational Intelligent Data Analysis for Sustainable Development : An Introduction and Overview / TING Yu ; NITESH V. CHAWLA ; SIMEO SIMOFF=1

SECTION I. INTEGRATED SUSTAINABILITY ANALYSIS

CHAPTER 2. Tracing Embodied CO₂ in Trade Using High-Resolution Input-Output Tables / DANIEL MORAN ; ARNE GESCHKE=27

CHAPTER 3. Aggregation Effects in Carbon Footprint Accounting Using Multi-Region Input-Output Analysis / XIN ZHOU ; HIROAKI SHIRAKAWA ; MANFRED LENZEN=53

SECTION II. COMPUTATIONAL INTELLIGENT DATA ANALYSIS FOR CLIMATE CHANGE

CHAPTER 4. Climate Informatics / CLAIRE MONTELEONI ; GAVIN A. SCHMIDT ; FRANCIS ALEXANDER ; ALEXANDRU NICULESCU-MIZIL ; KARSTEN STEHAEUSER ; MICHAEL TIPPETT ; ARINDAM BANERJEE ; M. BENNO BLUMENTHAL ; AUROOP R. GANCULY ; JASON E. SMERDON ; MARCO TEDESCO=81

CHAPTER 5. Computational Data Sciences for Actionable Insights on Climate Extremes and Uncertainty / AUROOP R. GAGULY ; EVAN KODRA ; SNIGDHANSU CHATTERIEE ; ARINDAM BANERJEE ; HABIB N. NAM=127

SECTION III. COMPUTATIONAL INTELLIGENT DATA ANALYSIS FOR BIODIVERSITY AND SPECIES CONSERVATION

CHAPTER 6. Mathematical Programming Applications to Land Conservation and Environmental Quality / JACOB R. FOOKS ; KENT D. MESSER=159

SECTION IV. COMPUTATIONAL INTELLIGENT DATA ANALYSIS FOR SMART GRID AND RENEWABLE ENERGY

CHAPTER 7. Data Analysis Challenges in the Future Energy Domain / FRANK EICHINGER ; DANIEL PATHMAPERUMA ; HARALD VOGT ; EMMAUEL MOLLER=181

CHAPTER 8. Electricity Supply without Fossil Fuels / JOHN BOLAND ; PETER PUDNEY ; JERZY FILAR=243

CHAPTER 9. Data Analysis for Real-Time Identification of Grid Disruptions / VARUN CHANDOLA ; OLUFEMI OMITAOMU ; STEVEN J. FERNANDEZ=273

CHAPTER 10. Statistical Approaches for Wind Resource Assessment / KALYAN VEERAMACHANENI ; XIANG YE ; UNA-MAY O'REILLY=303

SECTION V. COMPUTATIONAL INTELLIGENT DATA A NALYSIS FOR SOCIOPOLITICAL SUSTAINABILITY

CHAPTER 11. Spatia-Temporal Correlations in Criminal Offense Records / JAMESON L. TOOLE ; NATHAN EAGLE ; JOSHLA B. PLOTKIN=331

CHAPTER 12. Constraint and Optimization Techniques for Supporting Policy Making / MARCO GAVANELU ; FABRIZIO RIGUZZI ; MICHELA MILANO ; PAOLO CAGNOU=361

INDEX=383

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Computational intelligent data analysis for sustainable development 이용현황 표 - 등록번호, 청구기호, 권별정보, 자료실, 이용여부로 구성 되어있습니다.
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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 presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems.

With a focus on integrated sustainability analysis, the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas, revealing that aggregation errors have a high concentration over specific regions and sectors.

The book summarizes the latest contributions of the data analysis community to climate change research. A profuse amount of climate data of various types is available, providing a rich and fertile playground for future data mining and machine learning research. The book also pays special attention to several critical challenges in the science of climate extremes that are not handled by the current generation of climate models. It discusses potential conceptual and methodological directions to build a close integration between physical understanding, or physics-based modeling, and data-driven insights.

The book then covers the conservation of species and ecologically valuable land. A case study on the Pennsylvania Dirt and Gravel Roads Program demonstrates that multiple-objective linear programming is a more versatile and efficient approach than the widely used benefit targeting selection process.

Moving on to renewable energy and the need for smart grids, the book explores how the ongoing transformation to a sustainable energy system of renewable sources leads to a paradigm shift from demand-driven generation to generation-driven demand. It shows how to maximize renewable energy as electricity by building a supergrid or mixing renewable sources with demand management and storage. It also presents intelligent data analysis for real-time detection of disruptive events from power system frequency data collected using an existing Internet-based frequency monitoring network as well as evaluates a set of computationally intelligent techniques for long-term wind resource assessment.

In addition, the book gives an example of how temporal and spatial data analysis tools are used to gather knowledge about behavioral data and address important social problems such as criminal offenses. It also applies constraint logic programming to a planning problem: the environmental and social impact assessment of the regional energy plan of the Emilia-Romagna region of Italy.

Sustainable development problems, such as global warming, resource shortages, global species loss, and pollution, push researchers to create powerful data analysis approaches that analysts can then use to gain insight into these issues to support rational decision making. This volume shows both the data analysis and sustainable development communities how to use intelligent data analysis tools to address practical problems and encourages researchers to develop better methods.



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. This volume presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems.



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