Modeling of scour depth and length of a diversion channel flow system with soft computing techniques
This study employed soft computing techniques, namely, support vector machine (SVM) and Gaussian process regression (GPR) techniques, to predict the properties of a scour hole depth (ds) and length (Ls) in a diversion channel flow system. The study considered different geometries of diversion channe...
محفوظ في:
| المؤلفون الرئيسيون: | , , , |
|---|---|
| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
IWA Publishing
2023
|
| الوصول للمادة أونلاين: | http://psasir.upm.edu.my/id/eprint/110300/1/110300.pdf http://psasir.upm.edu.my/id/eprint/110300/ https://iwaponline.com/ws/article/23/3/1267/93399/Modeling-of-scour-depth-and-length-of-a-diversion |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| الملخص: | This study employed soft computing techniques, namely, support vector machine (SVM) and Gaussian process regression (GPR) techniques, to predict the properties of a scour hole depth (ds) and length (Ls) in a diversion channel flow system. The study considered different geometries of diversion channels (angles and bed widths) and different hydraulic conditions. Four kernel function models for each technique (polynomial kernel function, normalized polynomial kernel function, radial basis kernel, and the Pearson VII function kernel) were evaluated in this investigation. Root mean square error (RMSE) values are 8.3949 for training datasets and 11.6922 for testing datasets, confirming that the normalized polynomial kernel function-based GP outperformed other models in predicting Ls. Regarding predicting ds, the polynomial kernel function-based SVM outperforms other models, recording RMSE of 0.5175 for training datasets and 0.6019 for testing datasets. The sensitivity investigation of input parameters shows that the diversion angle had a major influence in predicting Ls and ds. |
|---|