Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors

The current study investigates the effect of a large climate index, such as NINO3, NINO3.4, NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an improved Adaptive Neuro Fuzzy Inference System (ANFIS) during 1987-2007. The bat algorithm (BA), particle swarm optimizat...

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Main Authors: Ehteram, M., Afan, H.A., Dianatikhah, M., Ahmed, A.N., Fai, C.M., Hossain, M.S., Allawi, M.F., Elshafie, A.
Format: Article
Language:English
Published: 2020
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spelling my.uniten.dspace-130172020-07-06T08:45:29Z Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors Ehteram, M. Afan, H.A. Dianatikhah, M. Ahmed, A.N. Fai, C.M. Hossain, M.S. Allawi, M.F. Elshafie, A. The current study investigates the effect of a large climate index, such as NINO3, NINO3.4, NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an improved Adaptive Neuro Fuzzy Inference System (ANFIS) during 1987-2007. The bat algorithm (BA), particle swarm optimization (PSO) and genetic algorithm (GA) were used to obtain the ANFIS parameter for the best ANFIS structure. Principal component analysis (PCA) and Varex rotation were used to decrease the number of effective components needed for the streamflow simulation. The results showed that the large climate index with six-month lag times had the best performance, and three components (PCA1, PCA2 and PCA3) were used to simulate the monthly streamflow. The results indicated that the ANFIS-BA had better results than the ANFIS-PSO and ANFIS-GA, with a root mean square error (RMSE) 25% and 30% less than the ANFIS-PSO and ANFIS-GA, respectively. In addition, the linear error in probability space (LEPS) score for the ANFIS-BA, based on the average values for the different months, was less than the ANFIS-PSO and ANFIS-GA. Furthermore, the uncertainty values for the different ANFIS models were used and the results indicated that the monthly simulated streamflow by the ANFIS was computed well at the 95% confidence level. It can be seen that the average streamflow for the summer season is 75 m3/s, so that the stream flow for summer, based on climate indexes, is more than that in other seasons. © 2019 by the authors. 2020-02-03T03:29:47Z 2020-02-03T03:29:47Z 2019 Article 10.3390/w11061130 en
institution Universiti Tenaga Nasional
building UNITEN Library
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continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description The current study investigates the effect of a large climate index, such as NINO3, NINO3.4, NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an improved Adaptive Neuro Fuzzy Inference System (ANFIS) during 1987-2007. The bat algorithm (BA), particle swarm optimization (PSO) and genetic algorithm (GA) were used to obtain the ANFIS parameter for the best ANFIS structure. Principal component analysis (PCA) and Varex rotation were used to decrease the number of effective components needed for the streamflow simulation. The results showed that the large climate index with six-month lag times had the best performance, and three components (PCA1, PCA2 and PCA3) were used to simulate the monthly streamflow. The results indicated that the ANFIS-BA had better results than the ANFIS-PSO and ANFIS-GA, with a root mean square error (RMSE) 25% and 30% less than the ANFIS-PSO and ANFIS-GA, respectively. In addition, the linear error in probability space (LEPS) score for the ANFIS-BA, based on the average values for the different months, was less than the ANFIS-PSO and ANFIS-GA. Furthermore, the uncertainty values for the different ANFIS models were used and the results indicated that the monthly simulated streamflow by the ANFIS was computed well at the 95% confidence level. It can be seen that the average streamflow for the summer season is 75 m3/s, so that the stream flow for summer, based on climate indexes, is more than that in other seasons. © 2019 by the authors.
format Article
author Ehteram, M.
Afan, H.A.
Dianatikhah, M.
Ahmed, A.N.
Fai, C.M.
Hossain, M.S.
Allawi, M.F.
Elshafie, A.
spellingShingle Ehteram, M.
Afan, H.A.
Dianatikhah, M.
Ahmed, A.N.
Fai, C.M.
Hossain, M.S.
Allawi, M.F.
Elshafie, A.
Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors
author_facet Ehteram, M.
Afan, H.A.
Dianatikhah, M.
Ahmed, A.N.
Fai, C.M.
Hossain, M.S.
Allawi, M.F.
Elshafie, A.
author_sort Ehteram, M.
title Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors
title_short Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors
title_full Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors
title_fullStr Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors
title_full_unstemmed Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors
title_sort assessing the predictability of an improved anfis model for monthly streamflow using lagged climate indices as predictors
publishDate 2020
_version_ 1672614199460626432
score 13.214268