Novel approach for estimation of sediment load in dam reservoir with hybrid intelligent algorithms
Karami Hojat; DadrasAjirlou Yashar; Jun Changhyun; Bateni Sayed M.; Band Shahab S.; Mosavi Amir; Moslehpour Massoud; Chau Kwok-Wing
MTMT : 32764401
Date : 2022
Journal title : Frontiers In Environmental Science
Journal volume : 10
Journal issue number : 6
Pages : 16.jan
Document type : folyóiratcikk
Subject : sediment load, sediment transport, river flow, machine learning, artificial intelligence, hydrological model, hydrology, big data, Természettudományok, Környezettudományok
Abstract :
Methods: Hence, this paper provides a new hybrid machine learning model called genetic support vector machine and analysis of variance (GSVMA). The analysis of variance (ANOVA) is known as the kernel function for the SVM algorithm. The proposed model is performed based on the Z-Alizadeh Sani dataset so that a genetic optimization algorithm is used to select crucial features. In addition, SVM with ANOVA, linear SVM (LSVM), and library for support vector machine (LIBSVM) with radial basis function (RBF) methods were applied to classify the dataset.