Computational Intelligent Data Analysis for Sustainable Development (Hardcover)

Ting Yu, Nitesh Chawla, Simeon Simoff

  • 出版商: CRC
  • 出版日期: 2013-04-04
  • 售價: $3,465
  • 貴賓價: 9.5$3,292
  • 語言: 英文
  • 頁數: 440
  • 裝訂: Hardcover
  • ISBN: 1439895945
  • ISBN-13: 9781439895948
  • 相關分類: Data Science
  • 立即出貨 (庫存=1)

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商品描述

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.

商品描述(中文翻譯)

在高度動態的計算智能數據分析領域中,研究人員設計算法來解決不斷變化的環境中越來越複雜的數據問題,包括經濟、環境和社會數據。《可持續發展的計算智能數據分析》提出了新的方法,用於自動處理這些類型的數據,以支持可持續發展的理性決策。通過眾多案例研究和應用,它展示了重要的數據分析方法,包括數學優化、機器學習、信號處理以及時間和空間分析,用於量化和描述可持續發展問題。

本書聚焦於綜合可持續性分析,提出了一種大規模二次規劃算法,將高分辨率的投入-產出表從國家規模擴展到跨國規模,以測量整個貿易供應鏈的碳足跡。它還量化了不同重新分類和聚合模式之間的誤差或分散,揭示了聚合誤差在特定地區和行業上的高濃度。

本書總結了數據分析界對氣候變化研究的最新貢獻。各種類型的豐富氣候數據提供了未來數據挖掘和機器學習研究的豐富和肥沃的場所。本書還特別關注當前一代氣候模型無法處理的幾個關鍵挑戰,並討論了潛在的概念和方法論方向,以建立物理理解或基於物理的建模與數據驅動洞察之間的緊密結合。

接下來,本書涵蓋了物種保護和生態價值土地。以賓夕法尼亞州的泥土和碎石道路計劃為例,展示了多目標線性規劃是比廣泛使用的效益定位選擇過程更靈活和高效的方法。

在可再生能源和智能電網的需求方面,本書探討了從需求驅動發電到發電驅動需求的可持續能源系統轉型所帶來的範式轉變。它展示了如何通過建立超級電網或將可再生能源與需求管理和儲能相結合,最大化可再生能源作為電力的使用。它還提出了基於現有的基於互聯網的頻率監測網絡收集的電力系統頻率數據的實時檢測的智能數據分析方法,以及評估一組計算智能技術用於長期風能資源評估。

此外,本書通過一個案例展示了如何使用時間和空間數據分析工具來獲取有關行為數據的知識,並解決重要的社會問題,如犯罪行為。它還將約束邏輯編程應用於規劃問題:意大利艾米利亞-羅馬涅地區的區域能源計劃的環境和社會影響評估。

可持續發展問題,如全球變暖、資源短缺、全球物種損失和污染,推動研究人員創造強大的數據分析方法,分析人員可以利用這些方法來深入了解這些問題,以支持理性決策。本書向數據分析和可持續發展社區展示了如何使用智能數據分析工具解決實際問題,並鼓勵研究人員開發更好的方法。