Department of Water Engineering, University of Jiroft, Jiroft, Iran.
Department of Hydrology and Water Resources, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Environ Pollut. 2020 Jul;262:114258. doi: 10.1016/j.envpol.2020.114258. Epub 2020 Mar 10.
Transient storage model (TSM) is the most popular model for simulating solutes transport in natural streams. Accurate estimate of TSM parameters is essential in many hydraulic and environmental problems. In this study, an improved version of high-level Moth-Swarm Algorithm (IMSA) was used to predict the TSM parameters. First, the performance of the improved model was successfully assessed through several benchmark functions. Next, a series of 58 measured hydraulic and geometric datasets was used to validate the model. The data were divided into two series randomly, 38 datasets were selected for derivation and the remaining 20 datasets were used to verification. Then the results of IMSA were compared with other algorithms proposed by previous researchers. Two statistical indices of root mean square error (RMSE) and coefficient of correlation (CC) were employed to evaluate the performance of the model. The results showed that despite the high complexity and uncertainty associated with the dispersion processes, the IMSA algorithm could accurately predict the TSM parameters.
瞬态存储模型（TSM）是模拟天然河流中溶质运移最常用的模型。准确估计 TSM 参数在许多水力和环境问题中至关重要。本研究采用改进的 moth-swarm 算法（IMSA）来预测 TSM 参数。首先，通过几个基准函数成功评估了改进模型的性能。接下来，使用一系列 58 个测量的水力和几何数据集对模型进行验证。这些数据随机分为两组，其中 38 个数据集用于推导，其余 20 个数据集用于验证。然后将 IMSA 的结果与先前研究人员提出的其他算法进行比较。采用均方根误差（RMSE）和相关系数（CC）两个统计指标来评估模型的性能。结果表明，尽管分散过程具有高度复杂性和不确定性，但 IMSA 算法仍能准确预测 TSM 参数。
