000			nam a22 7a 4500
999			_c28830 _d28830
008			180309b xxu||||| |||| 00| 0 eng d
020			_a9781138198692
082			_a338.9270285 _bYUT
100			_aYu, Ting
245			_aComputational intelligent data analysis for sustainable development
260			_bCRC Press, _a2013 _cBoca Raton:
300			_axviii, 414 p. _bill. _c24 cm.
365			_aGBP _b38.99, Rs. 3696.25
440			_aChapman &​ Hall/​CRC data mining and knowledge discovery series
520			_aGoing 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.
650			_aData Communications
650			_aClimate informatics
650			_aMathematical programming application
650			_aWind resource assessment
650			_aCriminal offense record
700			_aChawla, Nitesh V.
700			_aSimoff, Simeon J.
942			_2ddc _cBK