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.
Highlights
The regional understanding of the interrelationship between the catchment attributes and the catchment hydrologic responses could be one of the basic concepts to predict the hydrologic variables for any ungauged catchment [1]
The results indicated that the weekly streamflow predication based on the El Nino Southern Oscillation (ENSO) and equatorial indicant ocean oscillation (EQUINOO), with consideration to the current time, had a better performance than the climate indexes with lag times
Maity and Kashid [14] simulated weakly streamflow based on the input data of the ENSO, local outgoing radiation (LOR) and previous streamflow information based on genetic programming
Summary
The regional understanding of the interrelationship between the catchment attributes and the catchment hydrologic responses could be one of the basic concepts to predict the hydrologic variables for any ungauged catchment [1]. A catchment whichhydrological is not gauged for streamflow, implementing streamflow is considered oneFor of the most important variables that is essential, especiallya proper interrelationship is considered a real example of the needs for such regionalization for ungauged catchments. During the designand and interrelationship is could considered a real example the needs for[2] Such regionalization methodology construction of any hydrological or hydraulic structure, e.g., dams, barrages or bridges, several could be very valuable for a few motives [2]. Kashid et al [13] used genetic programming for a case study of the predication of streamflow with consideration to the ENSO and the equatorial indicant ocean oscillation (EQUINOO). The results indicated that the weekly streamflow predication based on the ENSO and EQUINOO, with consideration to the current time, had a better performance than the climate indexes with lag times. Different combinations of inputs with different lag times were used for the study
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