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Water 2018, 10(10), 1440; https://doi.org/10.3390/w10101440

Reducing Computational Costs of Automatic Calibration of Rainfall-Runoff Models: Meta-Models or High-Performance Computers?

1
Department of Geohydraulics and Engineering Hydrology, University of Kassel, Kurt-Wolters St. 3, 34109 Kassel, Germany
2
College of Engineering, University of Georgia, Athens, GA 30602, USA
3
Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße 84, 5374 Müncheberg, Germany
*
Author to whom correspondence should be addressed.
Received: 12 September 2018 / Revised: 28 September 2018 / Accepted: 3 October 2018 / Published: 12 October 2018
(This article belongs to the Section Hydrology)
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Abstract

Robust calibration of hydrologic models is critical for simulating water resource components; however, the time-consuming process of calibration sometimes impedes the accurate parameters’ estimation. The present study compares the performance of two approaches applied to overcome the computational costs of automatic calibration of the HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) model constructed for the Tamar basin located in northern Iran. The model is calibrated using the Particle Swarm Optimization (PSO) algorithm. In the first approach, a machine learning algorithm, i.e., Artificial Neural Network (ANN) was trained to act as a surrogate for the original HMS (ANN-PSO), while in the latter, the computational tasks were distributed among different processors. Due to inefficacy of preliminary ANN-PSO, an efficient adaptive technique was employed to boost training and accelerate the convergence of optimization. We found that both approaches were helpful in improving computational efficiency. For jointly-events calibrations schemes, meta-models outperformed parallelization due to effective exploration of calibration space, where parallel processing was not practical owing to the time required for data sharing and collecting among many clients. Model approximation using meta-models becomes highly complex, particularly in the presence of combining more events, because larger numbers of samples and much longer training times are required. View Full-Text
Keywords: parallel processing; Particle Swarm Optimization; machine learning; Artificial Neural Network; HEC-HMS parallel processing; Particle Swarm Optimization; machine learning; Artificial Neural Network; HEC-HMS
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Semiromi, M.T.; Omidvar, S.; Kamali, B. Reducing Computational Costs of Automatic Calibration of Rainfall-Runoff Models: Meta-Models or High-Performance Computers? Water 2018, 10, 1440.

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