Computational Intelligent Data Analysis for Sustainable Development

 

Published in 4th Apr 2013 as a part of “Data Mining and Knowledge Discovery Series” published by the Taylor & Francis

Publisher Link: http://www.crcpress.com/product/isbn/9781439895948

Amazon Link: http://www.amazon.com/Computational-Intelligent-Sustainable-Development-Knowledge/dp/1439895945

Edited by

Ting Yu, Honorary Research Fellow, University of Sydney, Australia,

Nitesh Chawla, Associate Professor, University of Notre Dame, USA

Simeon Simoff, Professor, University of Western Sydney, Australia

Summary:

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.

 

Table of Contents:

1.      Intelligent Data Analysis for Sustainable Development: An introduction and Overview

Ting Yu, Nitesh Chawla, and Simeon Simoff

 

Part 1: Integrated Sustainability Analysis

2.      Tracing Embodied CO2 in Trade Using High-Resolution Input-Output Tables

Daniel Moran and Arne Geschke

3.      Aggregation Effects in Carbon Footprint Accounting Using Multi-Region Input-Output Analysis

Xin Zhou, Hiroaki Shirakawa, and Manfred Lenzen

 

Part 2: Computational Intelligent Data Analysis for Climate Change

4.      Climate Informatics

Claire Monteleoni, Gavin A. Schmidt, Francis Alexander, Alexandru Niculescu-Mizil, Karsten Steinhaeuser, Michael Tippett, Arindam Banerjee, M. Benno Blumenthal, Auroop R. Ganguly, Jason E. Smerdon, and Marco Tedesco

5.      Computational Data Sciences for Actionable Insights on Climate Extremes and Uncertainty

Auroop R. Ganguly, Evan Kodra, Snigdhansu Chatterjee, Arindam Banerjee, and Habib N. Najm

 

Part 3: Computational Intelligent Data Analysis for Biodiversity and Species Conservation

6.      Mathematical Programming Applications to Land Conservation and Environmental Quality

Jacob R. Fooks and Kent D. Messer

 

Part 4: Computational Intelligent Data Analysis for Smart Grid and Renewable Energy

7.      Data Analysis Challenges in the Future Energy Domain

Frank Eichinger, Daniel Pathmaperuma, Harald Vogt, and Emmanuel Müller

8.      Electricity Supply without Fossil Fuels

John Boland, Peter Pudney, and Jerzy Filar

9.      Data Analysis for Real-Time Identification of Grid Disruptions

Varun Chandola, Olufemi Omitaomu, and Steven J. Fernandez

10.  Statistical Approaches for Wind Resource Assessment

Kalyan Veeramachaneni, Xiang Ye, and Una-May O’Reilly

 

Part 5: Computational Intelligent Data Analysis for Sociopolitical Sustainability

11.  Spatio-Temporal Correlations in Criminal Offense Records

Jameson L. Toole, Nathan Eagle, and Joshua B. Plotkin

12.  Constraint and Optimization Techniques for Supporting Policy Making

Marco Gavanelli, Fabrizio Riguzzi, Michela Milano, and Paolo Cagnoli

 

Audience for the Book

The primary audience for the edited book will be university professors, graduate students, researchers and professionals in both data analysis and sustainable development fields. Another audience would be government officials and policy makers interested in sustainability analysis.

Submission Procedure

Prospective authors should email Ting Yu at t.yu@physics.usyd.edu.au a copy of a 250 word proposed chapter abstract by August 15, 2011. Their chapter proposal should clearly outline the topic that the author(s) would like to examine and how the topic relates to the intelligent data analysis for sustainable development. Author(s) of accepted chapter proposals will be notified by September 15, 2011. Full chapters are due by December 15, 2011. All chapters will go through a double blind peer review process. Results of the peer reviews will be announced to authors by February 15, 2012. The final copy of their chapter will be due by April 1, 2012.

 

Advisory Board:

Manfred Lenzen, University of Sydney, Australia

Chris Dey, University of Sydney, Australia

Joy Murray, University of Sydney, Australia

Julien Ugon, University of Ballarat, Australia

Volker Wohlgemuth, Industrial Environmental Informatics, HTW Berlin, German

Pohsiang Tsai, National Formosa University, R.O. China

Aditya Ghose, University of Wollongong, Australia