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	Selection of relevant input variables in storm water quality modeling by multiobjective evolutionary polynomial regression paradigm
	Creaco E.; Berardi, L.; Sun, S. A.; Giustolisi, O.; Savic, D.
	2016
关键词	calibration data selection neural-network models resources applications turbidity measurements sensitivity performance algorithms prediction loads epr
英文摘要	The growing availability of field data, from information and communication technologies (ICTs) in "smart'' urban infrastructures, allows data modeling to understand complex phenomena and to support management decisions. Among the analyzed phenomena, those related to storm water quality modeling have recently been gaining interest in the scientific literature. Nonetheless, the large amount of available data poses the problem of selecting relevant variables to describe a phenomenon and enable robust data modeling. This paper presents a procedure for the selection of relevant input variables using the multi-objective evolutionary polynomial regression (EPR-MOGA) paradigm. The procedure is based on scrutinizing the explanatory variables that appear inside the set of EPR-MOGA symbolic model expressions of increasing complexity and goodness of fit to target output. The strategy also enables the selection to be validated by engineering judgement. In such context, the multiple case study extension of EPR-MOGA, called MCS-EPR-MOGA, is adopted. The application of the proposed procedure to modeling storm water quality parameters in two French catchments shows that it was able to significantly reduce the number of explanatory variables for successive analyses. Finally, the EPR-MOGA models obtained after the input selection are compared with those obtained by using the same technique without benefitting from input selection and with those obtained in previous works where other data-modeling techniques were used on the same data. The comparison highlights the effectiveness of both EPR-MOGA and the input selection procedure.
出处	Water Resources Research
卷	52
期	4
页	2403-2419
语种	英语
ISSN号	0043-1397
DOI标识	10.1002/2015wr017971
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/42901]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Creaco E.,Berardi, L.,Sun, S. A.,et al. Selection of relevant input variables in storm water quality modeling by multiobjective evolutionary polynomial regression paradigm. 2016.

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