Prediction of Monthly Streamflow Using Data-Driven Models

In recent years, data-driven modeling techniques have gained numerous applications in hydrology and water resources studies. River runoff estimation and forecasting is one of the research fields in which these techniques have several applications. In the current study, four data-driven modeling techniques of multiple linear regression, K-nearest neighbors, artificial neural networks, and adaptive neuro-fuzzy inference systems have been used to form runoff forecasting models and then their results have been evaluated. Also, effects of using some different scenarios to select predictor variables have been studied. It was evident from the results that using flow data related to one or two months ago in the predictor variables dataset can improve the accuracy of the results. In addition, comparison of general performances of the modeling techniques showed superiority of KNN models results among the studied models. The selected KNN model presented best performance with a linear correlation coefficient equal to 0.84 between observed flow data and predicted values and a RMSE equal to 2.64.