Linear, Nonlinear and Nonparametric Data Driven Models for River Flow Forecasting

What is it about?

In this 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.

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Why is it important?

The study can help to select appropriate data-driven method to provide efficient models for runoff forecasting.