Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting
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In recent years, the data-driven modeling techniques have gained more attention in hydrology and water resources studies. River runoff estimation and forecasting are one of the research fields that these techniques have several applications in them. In the current study, four common 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 for selecting predictor variables have been studied. It is evident from the results that using flow data of 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.
KeywordsRiver flow forecasting Data driven modeling MLR KNN ANN ANFIS
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Funding and Conflict of Interest
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Also, the authors declare that they do not have any conflict of interest.
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