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

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dc.citedby37
dc.contributor.authorEhteram M.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorDianatikhah M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorHossain M.S.en_US
dc.contributor.authorAllawi M.F.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57203893477en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid55579596900en_US
dc.contributor.authorid57057678400en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T07:25:25Z
dc.date.available2023-05-29T07:25:25Z
dc.date.issued2019
dc.descriptionClimate models; Climatology; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Mean square error; Particle swarm optimization (PSO); Principal component analysis; Stream flow; Adaptive neuro-fuzzy inference system; ANFIS-PSO; Climate index; Confidence levels; ENSO; Probability spaces; Root mean square errors; Streamflow simulations; Fuzzy inference; assessment method; El Nino-Southern Oscillation; genetic algorithm; index method; model; prediction; seasonal variation; streamflow; uncertainty analysisen_US
dc.description.abstractThe 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1130
dc.identifier.doi10.3390/w11061130
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85068820599
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85068820599&doi=10.3390%2fw11061130&partnerID=40&md5=288d2e4a95654283b20d9205375f6f1f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24643
dc.identifier.volume11
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleWater (Switzerland)
dc.titleAssessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictorsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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