Predicting multiple functions of sustainable flood retention basins under uncertainty via multi-instance multi-label learning

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

Abstract

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.

Citation

Yang, Q., Boehm, C., Scholz, M., Plant, C., & Shao, J. (2015). Predicting multiple functions of sustainable flood retention basins under uncertainty via multi-instance multi-label learning. Water, 7(4), 1359-1377. https://doi.org/10.3390/w7041359

• Journal Article Type: Article
• Acceptance Date: Mar 13, 2015
• Publication Date: Mar 26, 2015
• Deposit Date: Nov 23, 2016
• Publicly Available Date: Nov 19, 2019
• Journal: Water
• Publisher: MDPI
• Volume: 7
• Issue: 4
• Pages: 1359-1377
• DOI: https://doi.org/10.3390/w7041359
• Publisher URL: http://dx.doi.org/10.3390/w7041359
• Related Public URLs: https://www.mdpi.com/journal/water
• Additional Information: Funders : European Regional Development Fund Interreg IVB 2007–2013 North Sea Region Program;The University of Edinburgh;The University of Salford;Natural Science Foundation of China;Fundamental Research Funds for the Central Universities;Postdoctoral Science Foundation of China; Projects : Sustainable Flood Retention Basins to Control Flooding and Diffuse Pollution; Grant Number: 61403062; Grant Number: ZYGX2014J091; Grant Number: 2014M552344