Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series
(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
- Mohammadi, Babak LU ; Linh, Nguyen Thi Thuy ; Pham, Quoc Bao ; Ahmed, Ali Najah ; Vojteková, Jana ; Guan, Yiqing ; Abba, S. I. and El-Shafie, Ahmed
- publishing date
- 2020-07-26
- 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}}, }