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.
Highlights
Hydrologic process simulation models have brought about opportunities for watershed management policies and decision making analysis
To fill the above-mentioned lacuna, this study aims to compare the performance of two well-known techniques: meta-model and parallel processing to reduce the computational costs for the purpose of automatic calibration of the Hydrologic Engineering Center-Hydrologic Modeling Systems (HEC-HMS)
We found that since the performance of Artificial Neural Network (ANN) is adaptively enhanced for each iteration, the first ANN training can start with fewer sample size
Summary
Hydrologic process simulation models have brought about opportunities for watershed management policies and decision making analysis. Taking advantages of these models is highly dependent upon parameter estimation in a mechanism called “calibration”. Automatic calibrations are usually carried out through linking a simulation model (e.g., a hydrological model) with heuristic evolutionary optimization algorithms. Having not guaranteed finding the global solutions, evolutionary algorithms have been considered as efficient tools for the purpose of solving highly nonlinear or non-convex equations, which are the cases many modelers encounter. Despite advantages, implementing these techniques necessitates solving thousands of functions which results in the calibration procedure becomes time-consuming. It is inevitable to benefit from some state-of-the-art methods for reducing computational costs
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call