Bozorg-Haddad Omid, Sarzaeim Parisa, Loáiciga Hugo A
Department of Irrigation and Reclamation Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Tehran, 31587-77871, Iran.
Department of Geography, University of California, Santa Barbara, CA, 93016-4060, USA.
Sci Rep. 2021 Aug 10;11(1):16183. doi: 10.1038/s41598-021-95721-0.
The Muskingum model is a popular hydrologic flood routing technique; however, the accurate estimation of model parameters challenges the effective, precise, and rapid-response operation of flood routing. Evolutionary and metaheuristic optimization algorithms (EMOAs) are well suited for parameter estimation task associated with a wide range of complex models including the nonlinear Muskingum model. However, more proficient frameworks requiring less computational effort are substantially advantageous. Among the EMOAs teaching-learning-based optimization (TLBO) is a relatively new, parameter-free, and efficient metaheuristic optimization algorithm, inspired by the teacher-student interactions in a classroom to upgrade the overall knowledge of a topic through a teaching-learning procedure. The novelty of this study originates from (1) coupling TLBO and the nonlinear Muskingum routing model to estimate the Muskingum parameters by outflow predictability enhancement, and (2) evaluating a parameter-free algorithm's functionality and accuracy involving complex Muskingum model's parameter determination. TLBO, unlike previous EMOAs linked to the Muskingum model, is free of algorithmic parameters which makes it ideal for prediction without optimizing EMOAs parameters. The hypothesis herein entertained is that TLBO is effective in estimating the nonlinear Muskingum parameters efficiently and accurately. This hypothesis is evaluated with two popular benchmark examples, the Wilson and Wye River case studies. The results show the excellent performance of the "TLBO-Muskingum" for estimating accurately the Muskingum parameters based on the Nash-Sutcliffe Efficiency (NSE) to evaluate the TLBO's predictive skill using benchmark problems. The NSE index is calculated 0.99 and 0.94 for the Wilson and Wye River benchmarks, respectively.
马斯京根模型是一种常用的水文洪水演进技术；然而，模型参数的准确估计对洪水演进的有效、精确和快速响应运行提出了挑战。进化和元启发式优化算法（EMOAs）非常适合与包括非线性马斯京根模型在内的各种复杂模型相关的参数估计任务。然而，需要较少计算量的更高效框架具有显著优势。在EMOAs中，基于教学学习的优化（TLBO）是一种相对较新的、无参数且高效的元启发式优化算法，它受到课堂上师生互动的启发，通过教学过程提升对某一主题的整体知识。本研究的新颖之处在于：（1）将TLBO与非线性马斯京根演进模型相结合，通过提高流出量可预测性来估计马斯京根参数；（2）评估一种无参数算法在复杂马斯京根模型参数确定方面的功能和准确性。与之前与马斯京根模型相关的EMOAs不同，TLBO没有算法参数，这使其成为无需优化EMOAs参数即可进行预测的理想选择。本文提出的假设是，TLBO能够有效且准确地估计非线性马斯京根参数。通过两个流行的基准示例，即威尔逊河和怀伊河案例研究，对这一假设进行了评估。结果表明，基于纳什-萨特克利夫效率（NSE）来评估TLBO使用基准问题的预测技能时，“TLBO-马斯京根”在准确估计马斯京根参数方面表现出色。对于威尔逊河和怀伊河基准，NSE指数分别计算为0.99和0.94。
