Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure

Conventional neuro-fuzzy systems used for rainfall-runoff (R-R) modelling are generally dependent on expert knowledge. In these models, not only the structure is designed by the expert user, but also all the required knowledge for fuzzy partitioning of the input–output space and rule base need to be...

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Main Authors: Chang, Tak Kwin, Talei, Amin, Quek, Chai, Pauwels, Valentijn R. N.
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/139765
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1397652020-05-21T07:10:11Z Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure Chang, Tak Kwin Talei, Amin Quek, Chai Pauwels, Valentijn R. N. School of Computer Science and Engineering Centre for Computational Intelligence Engineering::Computer science and engineering Rainfall-runoff Modelling SaFIN Conventional neuro-fuzzy systems used for rainfall-runoff (R-R) modelling are generally dependent on expert knowledge. In these models, not only the structure is designed by the expert user, but also all the required knowledge for fuzzy partitioning of the input–output space and rule base need to be provided by the expert. To move towards NFS with a more flexible rule base and structure, efforts are made to integrate the self-reliant mechanisms into the learning algorithm that enable the model to identify the position and distribution of fuzzy labels in input–output space and generate the required rule base. In this study, the self-adaptive fuzzy inference network (SaFIN) is used for the R-R application. SaFIN employs a new clustering technique known as Categorical Learning-Induced Partitioning (CLIP) which allows the model to adapt to the new incoming tuple by consistently updating the model. SaFIN is also equipped with a rule-pruning mechanism that can exclude inconsistent and obsolete rules. In this study, SaFIN R-R models are developed in three different catchment types and sizes where the results are compared against a benchmark NFS model and few physical models including URHM, HBV, GR4J. Results shows that SaFIN is a capable and robust tool for R-R modeling under varying catchment conditions. Moreover, SaFIN produced comparable if not superior results to the benchmark models. It was concluded that SsFIN with its self-reliant learning and rule generation mechanism equipped with rule pruning can make it a competent tool for R-R modelling in catchments where the data may contain some inconsistencies. 2020-05-21T07:10:10Z 2020-05-21T07:10:10Z 2018 Journal Article Chang, T. K., Talei, A., Quek, C., & Pauwels, V. R. N. (2018). Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure. Journal of Hydrology, 564, 1179-1193. doi:10.1016/j.jhydrol.2018.07.074 0022-1694 https://hdl.handle.net/10356/139765 10.1016/j.jhydrol.2018.07.074 2-s2.0-85051461321 564 1179 1193 en Journal of Hydrology © 2018 Elsevier B.V. All rights reserved.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Rainfall-runoff Modelling
SaFIN
spellingShingle Engineering::Computer science and engineering
Rainfall-runoff Modelling
SaFIN
Chang, Tak Kwin
Talei, Amin
Quek, Chai
Pauwels, Valentijn R. N.
Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure
description Conventional neuro-fuzzy systems used for rainfall-runoff (R-R) modelling are generally dependent on expert knowledge. In these models, not only the structure is designed by the expert user, but also all the required knowledge for fuzzy partitioning of the input–output space and rule base need to be provided by the expert. To move towards NFS with a more flexible rule base and structure, efforts are made to integrate the self-reliant mechanisms into the learning algorithm that enable the model to identify the position and distribution of fuzzy labels in input–output space and generate the required rule base. In this study, the self-adaptive fuzzy inference network (SaFIN) is used for the R-R application. SaFIN employs a new clustering technique known as Categorical Learning-Induced Partitioning (CLIP) which allows the model to adapt to the new incoming tuple by consistently updating the model. SaFIN is also equipped with a rule-pruning mechanism that can exclude inconsistent and obsolete rules. In this study, SaFIN R-R models are developed in three different catchment types and sizes where the results are compared against a benchmark NFS model and few physical models including URHM, HBV, GR4J. Results shows that SaFIN is a capable and robust tool for R-R modeling under varying catchment conditions. Moreover, SaFIN produced comparable if not superior results to the benchmark models. It was concluded that SsFIN with its self-reliant learning and rule generation mechanism equipped with rule pruning can make it a competent tool for R-R modelling in catchments where the data may contain some inconsistencies.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Chang, Tak Kwin
Talei, Amin
Quek, Chai
Pauwels, Valentijn R. N.
format Article
author Chang, Tak Kwin
Talei, Amin
Quek, Chai
Pauwels, Valentijn R. N.
author_sort Chang, Tak Kwin
title Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure
title_short Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure
title_full Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure
title_fullStr Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure
title_full_unstemmed Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure
title_sort rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure
publishDate 2020
url https://hdl.handle.net/10356/139765
_version_ 1681058138749403136