A comparison of artificial intelligence approaches in predicting discharge coefficient of streamlined weirs
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Iwa Publishing
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In the present research, three different data-driven models (DDMs) are developed to predict the discharge coefficient of streamlined weirs (C-dstw). Some machine-learning methods (MLMs) and intelligent optimization models (IOMs) such as Random Forest (RF), Adaptive NeuroFuzzy Inference System (ANFIS), and gene expression program (GEP) methods are employed for the prediction of C-dstw. To identify input variables for the prediction of C-dstw by these DMMs, among potential parameters on C-dstw, the most effective ones including geometric features of streamlined weirs, relative eccentricity (lambda), downstream slope angle (beta), and water head over the crest of the weir (h(1)) are determined by applying Buckingham pi-theorem and cosine amplitude analyses. In this modeling, by changing architectures and fundamental parameters of the aforesaid approaches, many scenarios are defined to obtain ideal estimation results. According to statistical metrics and scatter plot, the GEP model is determined as a superior method to estimate C-dstw with high performance and accuracy. It yields an R-2 of 0.97, a Total Grade (TG) of 20, RMSE of 0.032, and MAE of 0.024. Besides, the generated mathematical equation for C-dstw in the best scenario by GEP is likened to the corresponding measured ones and the differences are within 0-10%.
Açıklama
Anahtar Kelimeler
Discharge Coefficient, Intelligent Optimization Models, Joukowsky Transform, Machine-Learning Models, Open-Channel Flow, Streamlined Weirs, Random Forests, Flow, Regression, Improve, Model, Kind
Kaynak
Journal of Hydroinformatics
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
25
Sayı
4
