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Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series

Mohammadi, Babak LU orcid ; Linh, Nguyen Thi Thuy ; Pham, Quoc Bao ; Ahmed, Ali Najah ; Vojteková, Jana ; Guan, Yiqing ; Abba, S. I. and El-Shafie, Ahmed (2020) In Hydrological Sciences Journal 65(10). p.1738-1751
Abstract

Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and accurate forecasting model for streamflow. In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog-leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input–output architecture were investigated. The results show that the proposed ANFIS-SFLA model (R2 = 0.88; NS = 0.88; RMSE... (More)

Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and accurate forecasting model for streamflow. In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog-leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input–output architecture were investigated. The results show that the proposed ANFIS-SFLA model (R2 = 0.88; NS = 0.88; RMSE = 142.30 (m3/s); MAE = 88.94 (m3/s); MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (R2 = 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83 (m3/s); MAPE = 45.97%). The proposed model could be generalized and applied in different rivers worldwide.

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author
; ; ; ; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
adaptive neuro-fuzzy inference system (ANFIS), estimation, shuffled frog leaping algorithm (SFLA), Streamflow, time series models
in
Hydrological Sciences Journal
volume
65
issue
10
pages
14 pages
publisher
Taylor & Francis
external identifiers
  • scopus:85086121489
ISSN
0262-6667
DOI
10.1080/02626667.2020.1758703
language
English
LU publication?
no
id
a9144ab4-0963-4c22-ab9c-bbfe050cd2c5
date added to LUP
2020-12-30 05:17:13
date last changed
2022-04-26 22:52:49
@article{a9144ab4-0963-4c22-ab9c-bbfe050cd2c5,
  abstract     = {{<p>Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and accurate forecasting model for streamflow. In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog-leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input–output architecture were investigated. The results show that the proposed ANFIS-SFLA model (R<sup>2</sup> = 0.88; NS = 0.88; RMSE = 142.30 (m<sup>3</sup>/s); MAE = 88.94 (m<sup>3</sup>/s); MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (R<sup>2</sup> = 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83 (m<sup>3</sup>/s); MAPE = 45.97%). The proposed model could be generalized and applied in different rivers worldwide.</p>}},
  author       = {{Mohammadi, Babak and Linh, Nguyen Thi Thuy and Pham, Quoc Bao and Ahmed, Ali Najah and Vojteková, Jana and Guan, Yiqing and Abba, S. I. and El-Shafie, Ahmed}},
  issn         = {{0262-6667}},
  keywords     = {{adaptive neuro-fuzzy inference system (ANFIS); estimation; shuffled frog leaping algorithm (SFLA); Streamflow; time series models}},
  language     = {{eng}},
  month        = {{07}},
  number       = {{10}},
  pages        = {{1738--1751}},
  publisher    = {{Taylor & Francis}},
  series       = {{Hydrological Sciences Journal}},
  title        = {{Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series}},
  url          = {{http://dx.doi.org/10.1080/02626667.2020.1758703}},
  doi          = {{10.1080/02626667.2020.1758703}},
  volume       = {{65}},
  year         = {{2020}},
}