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Saeed Sharafi

Saeed Sharafi

Academic rank: Associate Professor
ORCID: https://orcid.org/0000-0003-2644-5924
Education: PhD.
ScopusId: 26640694100
HIndex:
Faculty: Agriculture and Environment
Address: Arak University
Phone:

Research

Title
Evaluation of the least square support vector machines (LS-SVM) to predict longitudinal dispersion coefficient
Type
JournalPaper
Keywords
empirical equations, least square support vector machines, longitudinal dispersion coefficients, natural streams, performance
Year
2022
Journal Water Supply
DOI
Researchers Mehdi Mohammadi Ghaleni ، Mahmood Akbari ، Saeed Sharafi ، mohammad javad Nahvinia

Abstract

In this study, the least square support vector machines (LS-SVM) method was used to predict the longitudinal dispersion coefficient (DL) in natural streams in comparison with the empirical equations in various datasets. To do this, three datasets of field data including hydraulic and geometrical characteristics of different rivers, with various statistical characteristics were applied to evaluate the performance of LS-SVM and 15 empirical equations. The LS-SVM was evaluated and compared with developed empirical equations using statistical indices of root mean square error (RMSE), standard error (SE), mean bias error (MBE), discrepancy ratio (DR), Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2). The results demonstrated that LS-SVM method has the high capability to predict the DL in different datasets with RMSE = 58-82 m2 s-1, SE = 24-39 m2 s-1, MBE = -1.95-2.6 m2 s-1, DR = 0.08-0.13, R2 = 0.76-0.88, and NSE = 0.75-0.87 as compared with previous empirical equations. It can be concluded that the proposed LS-SVM model can be successfully applied to predict the DL for a wide range of river characteristics.