Estimation of the longitudinal dispersion coefficient via a fusion of optimized models. Issue 3 (29th March 2022)

• Record Type: Journal Article
• Authors: Gholami, Mahsa; Gholami, Amin; Singh, Vijay P.
• Is Part Of: Journal of hydroinformatics. Volume 24:Issue 3(2022)
• Page Start: 517
• Page End: 534
• Publication Date: 2022-03-29
• Subjects: Bat-inspired algorithm (BA) -- committee machine (CM) -- longitudinal dispersion coefficient (LDC) -- optimized models (OMs) -- river systems -- sensitivity analysis (SA); Hydrology -- Data processing -- Periodicals; Geographic information systems -- Periodicals; Geographic information systems; Hydrology -- Data processing; Electronic journals; Periodicals; 551.480285
• DOI: 10.2166/hydro.2022.092
• Languages: English
• ISSNs: 1464-7141

Abstract

Abstract: Determination of the longitudinal dispersion coefficient (LDC) is fundamental to the development of strategies for environmental management of river systems. This paper presents an integrated model for an estimation of the longitudinal dispersion coefficient by a fusion of optimized intelligent models (optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR)) via committee machine (CM), with optimization done by the Bat-inspired algorithm (BA). The optimization eliminates the associated loss of accuracy of the intelligent models, which is a direct consequence of an improper adjustment of parameters (weights and biases in the neural network, membership's functions in the fuzzy inference system, and user-defined parameters in support vector regression). Data gathered from literature are employed to validate the proposed integrated model. A comparison between the optimized models and a committee machine, based on statistical parameters, shows that the committee machine model can attain high accuracy. Sensitivity analysis (SA) shows the contribution of each optimized model to the committee machine and ranks the contribution of the optimized models in ascending order as optimized neural network, optimized fuzzy inference system, and optimized support vector regression, each significantly correlated with the accuracy of longitudinal dispersion coefficient prediction. HIGHLIGHTS: In this study, the longitudinal dispersion coefficient (LDC) is modeled. Optimized models (OMs; OSVR, ONN, and OFIS) are employed for modeling purposes. The bat-inspired algorithm (BA) is utilized for improving intelligent models. The OMs are integrated by a committee machine (CM) with a BA combiner. The CM generates outstanding results. Graphical Abstract