Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran.
Water Security and Sustainable Development Hub, School of Engineering, Newcastle University, Newcastle upon Tyne, UK.
Environ Sci Pollut Res Int. 2021 Jul;28(27):35971-35990. doi: 10.1007/s11356-021-12651-0. Epub 2021 Mar 8.
The longitudinal dispersion coefficient (LDC) of river pollutants is considered as one of the prominent water quality parameters. In this regard, numerous research studies have been conducted in recent years, and various equations have been extracted based on hydrodynamic and geometric elements. LDC's estimated values obtained using different equations reveal a significant uncertainty due to this phenomenon's complexity. In the present study, the crow search algorithm (CSA) is applied to increase the equation's precision by employing evolutionary polynomial regression (EPR) to model an extensive amount of geometrical and hydraulic data. The results indicate that the CSA improves the performance of EPR in terms of R (0.8), Willmott's index of agreement (0.93), Nash-Sutcliffe efficiency (0.77), and overall index (0.84). In addition, the reliability analysis of the proposed equation (i.e., CSA) reduced the failure probability (P) when the value of the failure state containing 50 to 600 m/s is increasing for the P determination using the Monte Carlo simulation. The best-fitted function for correct failure probability prediction was the power with R = 0.98 compared with linear and exponential functions.
河流污染物的纵向离散系数（LDC）被认为是重要的水质参数之一。近年来，已经进行了许多相关研究，并基于水动力和几何要素推导出了各种方程。由于这种现象的复杂性，不同方程估算的 LDC 值存在显著的不确定性。在本研究中，采用 Crow Search Algorithm（CSA）通过进化多项式回归（EPR）对大量几何和水力数据进行建模，以提高方程的精度。结果表明，CSA 提高了 EPR 的性能，其 R 值为 0.8，Willmott 一致性指数为 0.93，纳什-苏特克利夫效率为 0.77，整体指数为 0.84。此外，利用蒙特卡罗模拟法，当失败状态值在 50 至 600 m/s 之间增加时，所提出的方程（即 CSA）的可靠性分析降低了失败概率（P）。与线性和指数函数相比，幂函数具有最佳的拟合函数，其 R 值为 0.98，可用于正确预测失败概率。
