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
Megjelenés dátuma : 2022
Folyóirat címe : Frontiers In Environmental Science
Évfolyam : 10
Szám : 6
Oldalszám : 16.jan
Dokumentum típusa : folyóiratcikk
Kulcsszó : sediment load, sediment transport, river flow, machine learning, artificial intelligence, hydrological model, hydrology, big data, Természettudományok, Környezettudományok
Absztrakt :
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.
