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

Mohammad Ehteram, Haitham Abdulmohsin Afan, Mojgan Dianatikhah, Ali Najah Ahmed, Chow Ming Fai, Md Shabbir Hossain, Mohammed Falah Allawi, Ahmed Elshafie

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number1130
JournalWater (Switzerland)
Volume11
Issue number6
DOIs
Publication statusPublished - 01 Jun 2019

Fingerprint

Fuzzy inference
stream flow
system model
Climate
streamflow
climate
genetic algorithm
swarms
bat
Chiroptera
Particle swarm optimization (PSO)
Genetic algorithms
Stream flow
summer
Iran
index
Principal Component Analysis
principal component analysis
Uncertainty
uncertainty

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology

Cite this

Ehteram, Mohammad ; Afan, Haitham Abdulmohsin ; Dianatikhah, Mojgan ; Ahmed, Ali Najah ; Fai, Chow Ming ; Hossain, Md Shabbir ; Allawi, Mohammed Falah ; Elshafie, Ahmed. / Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors. In: Water (Switzerland). 2019 ; Vol. 11, No. 6.
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abstract = "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.",
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Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors. / Ehteram, Mohammad; Afan, Haitham Abdulmohsin; Dianatikhah, Mojgan; Ahmed, Ali Najah; Fai, Chow Ming; Hossain, Md Shabbir; Allawi, Mohammed Falah; Elshafie, Ahmed.

In: Water (Switzerland), Vol. 11, No. 6, 1130, 01.06.2019.

Research output: Contribution to journalArticle

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