A novel equation for longitudinal dispersion coefficient prediction based on the hybrid of SSMD and whale optimization algorithm

The purpose of the present paper is improving the accuracy of existing formulas for the longitudinal dispersion coefficient (LDC) prediction based on a novel and simple meta-heuristic optimization method called Whale Optimization Algorithm (WOA). Although several existing formulas calculate LDC in the rivers based on the hydraulic and hydrodynamic specifications, most of them have significant errors in confronting extensive field data. In this study, comprehensive field data, including the geometrical and hydraulic properties of different rivers in the world, were adopted to build a reliable model. Statistical error measures were used to evaluate and compare the results with other studies. Furthermore, the Subset Selection of Maximum Dissimilarity (SSMD) method was utilized for a reputable selection of data for training and testing the WOA model. Subset selection is a critical factor in artificial intelligence (AI) computations. Finally, an integrated model based on the SSMD method and WOA technique has been proposed to develop the high accuracy formulas for the prediction of LDC. According to the results, the developed formulas are competitive or superior to the previous formulas for LDC estimation. Results also indicated that the WOA algorithm could be applied to improve the performance of the predictive equations in other fields of studies by finding the optimum values of coefficients. [Display omitted] •Three novel equations for LDC estimation were proposed.•SSMD is used for reproducing train and test subsets with almost the same statistical characteristics.•SSMD-WOA is utilized to find optimal equations for LDC estimation.•The proposed equations based on the hybridized SSMD and WOA are superior to the all previously proposed equations in LDC estimation.