Research Area: Metaheuristic Computing
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 R2 (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 (Pf) when the value of the failure state containing 50 to 600 m2/s is increasing for the Pf determination using the Monte Carlo simulation. The best-fitted function for correct failure probability prediction was the power with R2 = 0.98 compared with linear and exponential functions.
Keywords:
longitudinal dispersion coefficient
river pollutant
water quality parameters
hydrodynamic
geometric elements
polynomial regression
failure probability
Author(s) Name: Alireza Ghaemi, Tahmineh Zhian, Bahareh Pirzadeh, Seyedarman Hashemi Monfared & Amir Mosavi
Journal name: Environmental Science and Pollution Research
Conferrence name:
Publisher name: Springer
DOI: 10.1007/s11356-021-12651-0
Volume Information: 28, pages 35971–35990
