Prediction of Monthly Streamflow Using Data-driven Models

Document Type : Technical Note (5 pages)

Authors

1 PhD Student, Department of Civil, Water and Environmental Engineering, Shahid Beheshti University

2 Assistant Professor, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University

Abstract

In recent years, data-driven modeling techniques have gained several 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, including 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 of some different scenarios to select predictor variables have been studied. It is evident from the results that using flow data related to one or two month ago in the predictor variables dataset can improve accuracy of results. In addition, comparison of general performances of the modeling techniques shows superiority of results of KNN models among the studied models. Among selected models of the different techniques, 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.

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Main Subjects

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Iran-Water Resources Research
Volume 13, Issue 2
September 2017
Pages 207-214

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History

  • Receive Date: 11 March 2016
  • Revise Date: 28 July 2016
  • Accept Date: 28 July 2016

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