Simultaneous streamflow forecasting based on hybridized neuro-fuzzy method for a river system

Assessment in simulating river flows for a river system can be implemented simultaneously. In general, the majority of the researchers emphasize forecasting on single output for a river system. The present study investigates the applicability and capability of coactive neuro-fuzzy inference system (CANFIS) for simultaneous river flow forecasting for a Barak river system in Assam, India. Besides, two other hybrid model approaches were developed by optimizing the parameters of CANFIS to adopt model’s consistency toward achieving more precise and sensitive result which includes the combination of the CANFIS using genetic algorithm (CANFIS-GA) and CANFIS using firefly algorithm (CANFIS-FA). In total, 19,728 sets of recorded hourly concurrent flows data have been collected from different gauging sites pertaining to monsoon seasons. The results of the models (CANFIS, CANFIS-GA and CANFIS-FA) are evaluated, and the best-fit forecasting model(s) is determined using various statistical performance criterions. Also, this study witnessed the significant improvement in the quality of flow forecasting of traditional CANFIS when integrated using metaheuristics algorithms GA and FA. Besides, performance comparisons of the models are made using the artificial neural networks and probabilistics neural networks. In overall, results suggested that CANFIS-FA considerably improved upon other models and provides more better and accurate results for simultaneous flow forecasting in a river system.