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Computational Intelligent Data Analysis for Sustainable Development (Paperback)
Loot Price: R1,915
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Computational Intelligent Data Analysis for Sustainable Development (Paperback)
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Expected to ship within 12 - 19 working days
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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.
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