A Comparative Study on Prediction of Monthly Streamflow Using Hybrid ANFIS-PSO Approaches

Abstract

Monthly prediction of streamflow is a fundamental and complex hydrological phenomenon. Accurate streamflow prediction helps in water resources planning, design, and management, particularly for hydropower production, irrigation, protection of dams and flood risk management. Hence, in this paper, a hybrid robust model integrating adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) algorithm was developed for monthly streamflow prediction of Barak River basin, India. Multiple factors like Precipitation (Pt), temperature (Tt), humidity (Ht), Infiltration loss (It), are considered as the inputs for determining the streamflow. For validating the model performance, 70% of data (1980–2007) were used for training them and 30% of data (2008–2019) were used for testing them. A comparison is made between results of developed hybrid model with simple artificial neural network (ANN) and ANFIS models for assessing accuracy and efficiency of model performances. Obtained results of proposed models were evaluated based on four assessment indices including by Root Mean Square Error (RMSE), Mean Absolute Error (MAE), determination coefficient (R2) and Nash-Sutcliffe Coefficient (ENS). Based on comparison of results, it was concluded that robust ANFIS-PSO model with RMSE = 5.887, MAE = 4.978, R2 = 0.9668, and ENS = 0.961 demonstrated best performance with more reliability and accuracy in comparison to ANFIS and ANN models. Findings of this research proved that hybrid ANFIS with an evolutionary optimization algorithm is a reliable modelling approach for monthly streamflow prediction.