Feature selection methods for characterizing and classifying adaptive sustainable flood retention basins

Yang, Q; Shao, J; Scholz, M; Plant, C

Abstract

The European Union’s Flood Directive 2007/60/EC requires member states to produce flood
risk maps for all river basins and coastal areas at risk of flooding by 2013. As a result, flood
risk assessments have become an urgent challenge requiring a range of rapid and effective
tools and approaches. The Sustainable Flood Retention Basin (SFRB) concept has evolved to
provide a rapid assessment technique for impoundments, which have a pre-defined or
potential role in flood defense and diffuse pollution control. A previous version of the SFRB
survey method developed by the co-author Scholz in 2006 recommends gathering of over
40 variables to characterize an SFRB. Collecting all these variables is relatively timeconsuming
and more importantly, these variables are often correlated with each other.
Therefore, the objective is to explore the correlation among these variables and find the
most important variables to represent an SFRB. Three feature selection techniques
(Information Gain, Mutual Information and Relief) were applied on the SFRB data set to
identify the importance of the variables in terms of classification accuracy. Four benchmark
classifiers (Support Vector Machine, K-Nearest Neighbours, C4.5 Decision Tree and
Naı¨ve Bayes) were subsequently used to verify the effectiveness of the classification with
the selected variables and automatically identify the optimal number of variables. Experimental
results indicate that our proposed approach provides a simple, rapid and effective
framework for variable selection and SFRB classification. Only nine important variables are
sufficient to accurately classify SFRB. Finally, six typical cases were studied to verify the
performance of the identified nine variables on different SFRB types. The findings provide
a rapid scientific tool for SFRB assessment in practice. Moreover, the generic value of this
tool allows also for its wide application in other areas.

Citation

Yang, Q., Shao, J., Scholz, M., & Plant, C. (2011). Feature selection methods for characterizing and classifying adaptive sustainable flood retention basins. Water Research, 45(3), 993-1004. https://doi.org/10.1016/j.watres.2010.10.006

• Journal Article Type: Article
• Publication Date: Jan 1, 2011
• Deposit Date: Dec 22, 2011
• Journal: Water Research
• Print ISSN: 0043-1354
• Publisher: IWA Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 45
• Issue: 3
• Pages: 993-1004
• DOI: https://doi.org/10.1016/j.watres.2010.10.006
• Publisher URL: http://dx.doi.org/10.1016/j.watres.2010.10.006