Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling

A. Talei; L. H.C. Chua; C. Quek

Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling

A. Talei, L. H.C. Chua, C. Quek

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

Abstract

Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) is a Takagi-Sugeno-type fuzzy inference system for online learning which can be applied for dynamic time series prediction. To the best of our knowledge, this is the first time that DENFIS has been used for rainfall-runoff (R-R) modeling. DENFIS model results were compared to the results obtained from the physically-based Storm Water Management Model (SWMM) and an Adaptive Network-based Fuzzy Inference System (ANFIS) which employs offline learning. Data from a small (5.6 km²) catchment in Singapore, comprising 11 separated storm events were analyzed. Rainfall was the only input used for the DENFIS and ANFIS models and the output was discharge at the present time. It is concluded that DENFIS results are better or at least comparable to SWMM, but similar to ANFIS. These results indicate a strong potential for DENFIS to be used in R-R modeling.

Original language	English
Title of host publication	34th IAHR Congress 2011 - Balance and Uncertainty
Subtitle of host publication	Water in a Changing World, Incorporating the 33rd Hydrology and Water Resources Symposium and the 10th Conference on Hydraulics in Water Engineering
Publisher	International Association for Hydro-Environment Engineering and Research (IAHR)
Pages	1514-1521
Number of pages	8
ISBN (Electronic)	9780858258686
Publication status	Published - 2011
Externally published	Yes
Event	Congress of IAHR, the International Association of Hydro-Environment Engineering and Research 2011 - Brisbane, Australia Duration: 26 Jun 2011 → 1 Jul 2011 Conference number: 34th

Conference

Conference	Congress of IAHR, the International Association of Hydro-Environment Engineering and Research 2011
Country/Territory	Australia
City	Brisbane
Period	26/06/11 → 1/07/11
Other	Proceedings of the 34th IAHR World Congress [electronic resource] : 33rd Hydrology and Water Resources Symposium : 10th Conference on Hydraulics in Water Engineering, 26 June - 1 July 2011, Brisbane Australia

Keywords

DENFIS
Neuro-Fuzzy
Rainfall-Runoff Modelling

Cite this

Talei, A., Chua, L. H. C., & Quek, C. (2011). Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling. In 34th IAHR Congress 2011 - Balance and Uncertainty: Water in a Changing World, Incorporating the 33rd Hydrology and Water Resources Symposium and the 10th Conference on Hydraulics in Water Engineering (pp. 1514-1521). International Association for Hydro-Environment Engineering and Research (IAHR).

Talei, A. ; Chua, L. H.C. ; Quek, C. / Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling. 34th IAHR Congress 2011 - Balance and Uncertainty: Water in a Changing World, Incorporating the 33rd Hydrology and Water Resources Symposium and the 10th Conference on Hydraulics in Water Engineering. International Association for Hydro-Environment Engineering and Research (IAHR), 2011. pp. 1514-1521

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title = "Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling",

abstract = "Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) is a Takagi-Sugeno-type fuzzy inference system for online learning which can be applied for dynamic time series prediction. To the best of our knowledge, this is the first time that DENFIS has been used for rainfall-runoff (R-R) modeling. DENFIS model results were compared to the results obtained from the physically-based Storm Water Management Model (SWMM) and an Adaptive Network-based Fuzzy Inference System (ANFIS) which employs offline learning. Data from a small (5.6 km2) catchment in Singapore, comprising 11 separated storm events were analyzed. Rainfall was the only input used for the DENFIS and ANFIS models and the output was discharge at the present time. It is concluded that DENFIS results are better or at least comparable to SWMM, but similar to ANFIS. These results indicate a strong potential for DENFIS to be used in R-R modeling.",

keywords = "DENFIS, Neuro-Fuzzy, Rainfall-Runoff Modelling",

author = "A. Talei and Chua, {L. H.C.} and C. Quek",

note = "Publisher Copyright: {\textcopyright} 34th IAHR Congress 2011. All rights reserved. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; Congress of IAHR, the International Association of Hydro-Environment Engineering and Research 2011 ; Conference date: 26-06-2011 Through 01-07-2011",

year = "2011",

language = "English",

pages = "1514--1521",

booktitle = "34th IAHR Congress 2011 - Balance and Uncertainty",

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Talei, A, Chua, LHC & Quek, C 2011, Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling. in 34th IAHR Congress 2011 - Balance and Uncertainty: Water in a Changing World, Incorporating the 33rd Hydrology and Water Resources Symposium and the 10th Conference on Hydraulics in Water Engineering. International Association for Hydro-Environment Engineering and Research (IAHR), pp. 1514-1521, Congress of IAHR, the International Association of Hydro-Environment Engineering and Research 2011, Brisbane, Queensland, Australia, 26/06/11.

Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling. / Talei, A.; Chua, L. H.C.; Quek, C.

34th IAHR Congress 2011 - Balance and Uncertainty: Water in a Changing World, Incorporating the 33rd Hydrology and Water Resources Symposium and the 10th Conference on Hydraulics in Water Engineering. International Association for Hydro-Environment Engineering and Research (IAHR), 2011. p. 1514-1521.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

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T1 - Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling

AU - Talei, A.

AU - Chua, L. H.C.

AU - Quek, C.

N1 - Conference code: 34th

PY - 2011

Y1 - 2011

N2 - Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) is a Takagi-Sugeno-type fuzzy inference system for online learning which can be applied for dynamic time series prediction. To the best of our knowledge, this is the first time that DENFIS has been used for rainfall-runoff (R-R) modeling. DENFIS model results were compared to the results obtained from the physically-based Storm Water Management Model (SWMM) and an Adaptive Network-based Fuzzy Inference System (ANFIS) which employs offline learning. Data from a small (5.6 km2) catchment in Singapore, comprising 11 separated storm events were analyzed. Rainfall was the only input used for the DENFIS and ANFIS models and the output was discharge at the present time. It is concluded that DENFIS results are better or at least comparable to SWMM, but similar to ANFIS. These results indicate a strong potential for DENFIS to be used in R-R modeling.

AB - Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) is a Takagi-Sugeno-type fuzzy inference system for online learning which can be applied for dynamic time series prediction. To the best of our knowledge, this is the first time that DENFIS has been used for rainfall-runoff (R-R) modeling. DENFIS model results were compared to the results obtained from the physically-based Storm Water Management Model (SWMM) and an Adaptive Network-based Fuzzy Inference System (ANFIS) which employs offline learning. Data from a small (5.6 km2) catchment in Singapore, comprising 11 separated storm events were analyzed. Rainfall was the only input used for the DENFIS and ANFIS models and the output was discharge at the present time. It is concluded that DENFIS results are better or at least comparable to SWMM, but similar to ANFIS. These results indicate a strong potential for DENFIS to be used in R-R modeling.

KW - DENFIS

KW - Neuro-Fuzzy

KW - Rainfall-Runoff Modelling

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BT - 34th IAHR Congress 2011 - Balance and Uncertainty

PB - International Association for Hydro-Environment Engineering and Research (IAHR)

T2 - Congress of IAHR, the International Association of Hydro-Environment Engineering and Research 2011

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ER -

Talei A, Chua LHC, Quek C. Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling. In 34th IAHR Congress 2011 - Balance and Uncertainty: Water in a Changing World, Incorporating the 33rd Hydrology and Water Resources Symposium and the 10th Conference on Hydraulics in Water Engineering. International Association for Hydro-Environment Engineering and Research (IAHR). 2011. p. 1514-1521

Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling

Abstract

Conference

Keywords

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Cite this