Abstract
This study evaluates the performances of two distinct linear and non-linear models for simulating non-linear rainfall–runoff processes and their applications to flood forecasting in the Navrood River basin, Iran. Due to the excellent capacity of the artificial neural networks [multilayer perceptron (MLP)] and Volterra model, these models were used to approximate arbitrary non-linear rainfall–runoff processes. The MLP model was trained using two different training algorithms. The Volterra model was applied as a linear model [the first-order Volterra (FOV) model] and solved using the traditional ordinary least-square (OLS) method. Storm events within the Navrood River basin were used to verify the suitability of the two models. The models’ performances were evaluated and compared using five performance criteria namely coefficient of efficiency, root mean square error, error of total volume, relative error of peak discharge, and error of time for peak to arrive. Results indicated that the non-linear MLP models outperform the linear FOV model. The latter was ineffective because of the non-linearity of the rainfall–runoff process. Moreover, the OLS method is inefficient when the FOV model has many parameters that must be estimated.
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We thank Dr. Asadi for helping in collecting data. Thanks are also extended to the anonymous reviewers and editor (Dr. John Carranza) for their constructive and useful comments that helped us improve the quality of the paper.
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Hasanpour Kashani, M., Ghorbani, M.A., Dinpashoh, Y. et al. Comparison of Volterra Model and Artificial Neural Networks for Rainfall–Runoff Simulation. Nat Resour Res 23, 341–354 (2014). https://doi.org/10.1007/s11053-014-9235-y
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DOI: https://doi.org/10.1007/s11053-014-9235-y