Predicting Multiple Functions of Sustainable Flood Retention Basins under Uncertainty via Multi-Instance Multi-Label Learning

Water

Qinli Yang¹  Christian Boehm⁵  Miklas Scholz²  Claudia Plant³  Junming Shao⁴ 

[1] School of Resources and Environment, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West High-Tech Zone, Chengdu 611731, China; E-Mail:;Civil Engineering Research Group, School of Computing, Science and Engineering, The University of Salford, Salford M5 4WT, UK; E-Mail:;Helmholtz Zentrum Munich, German Research Center for Environmental Health, Neuherberg 85764, Germany; E-Mail:;School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West High-Tech Zone, Chengdu 611731, China;Institute for Computer Science, University of Munich, Munich 80937, Germany; E-Mail:

关键词: sustainable flood retention basin;    function assessment;    uncertainty;    multi-instance multi-label learning;    classification;   

DOI  :  10.3390/w7041359

来源: mdpi

【 摘 要 】

The ambiguity of diverse functions of sustainable flood retention basins (SFRBs) may lead to conflict and risk in water resources planning and management. How can someone provide an intuitive yet efficient strategy to uncover and distinguish the multiple potential functions of SFRBs under uncertainty? In this study, by exploiting both input and output uncertainties of SFRBs, the authors developed a new data-driven framework to automatically predict the multiple functions of SFRBs by using multi-instance multi-label (MIML) learning. A total of 372 sustainable flood retention basins, characterized by 40 variables associated with confidence levels, were surveyed in Scotland, UK. A Gaussian model with Monte Carlo sampling was used to capture the variability of variables (i.e., input uncertainty), and the MIML-support vector machine (SVM) algorithm was subsequently applied to predict the potential functions of SFRBs that have not yet been assessed, allowing for one basin belonging to different types (i.e., output uncertainty). Experiments demonstrated that the proposed approach enables effective automatic prediction of the potential functions of SFRBs (e.g., accuracy >93%). The findings suggest that the functional uncertainty of SFRBs under investigation can be better assessed in a more comprehensive and cost-effective way, and the proposed data-driven approach provides a promising method of doing so for water resources management.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.