Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors
Climate 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 m...
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my.uniten.dspace-246432023-05-29T15:25:25Z 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. 57113510800 56436626600 57203893477 57214837520 57214146115 55579596900 57057678400 16068189400 Climate 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 analysis 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. Final 2023-05-29T07:25:25Z 2023-05-29T07:25:25Z 2019 Article 10.3390/w11061130 2-s2.0-85068820599 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068820599&doi=10.3390%2fw11061130&partnerID=40&md5=288d2e4a95654283b20d9205375f6f1f https://irepository.uniten.edu.my/handle/123456789/24643 11 6 1130 All Open Access, Gold MDPI AG Scopus |
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Climate 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 analysis |
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57113510800 |
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57113510800 Ehteram M. Afan H.A. Dianatikhah M. Ahmed A.N. Fai C.M. Hossain M.S. Allawi M.F. Elshafie A. |
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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_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 |
| publisher |
MDPI AG |
| publishDate |
2023 |
| _version_ |
1806427632641769472 |
| score |
13.211869 |