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Open AccessArticle

Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors

1
Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran
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Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia
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Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional, 43000 Selangor, Malaysia
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School of Energy, Geoscience, Infrastructure and Society. Department of Civil Engineering, Heriot-Watt University, Putrajaya 62200, Malaysia
5
Civil and Structural Engineering Department, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor 43600, Malaysia
*
Author to whom correspondence should be addressed.
Water 2019, 11(6), 1130; https://doi.org/10.3390/w11061130
Received: 23 April 2019 / Revised: 9 May 2019 / Accepted: 13 May 2019 / Published: 29 May 2019
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. View Full-Text
Keywords: ANFIS-BA; ANFIS-PSO; ANFIS-GA; large climate index; ENSO ANFIS-BA; ANFIS-PSO; ANFIS-GA; large climate index; ENSO
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Ehteram, M.; Afan, H.A.; Dianatikhah, M.; Ahmed, A.N.; Ming Fai, C.; 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. Water 2019, 11, 1130.

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