Hybrid model of support vector regression and fruitfly optimization algorithm for predicting ski-jump spillway scour geometry

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Xinpo Sun - , Sichuan University of Science & Engineering (Author)
  • Yuzhang Bi - , Nanjing Nanda Geotechnical Engineering Technology Co. Ltd. (Author)
  • Hojat Karami - , Semnan University (Author)
  • Shayan Naini - , Semnan University (Author)
  • Shahab S. Band - , Duy Tan University, National Yunlin University of Science and Technology (Author)
  • Amir Mosavi - , Óbuda University, Norwegian University of Life Sciences (Author)

Abstract

Accurate prediction of the scour hole depth and dimensions downstream of ski-jump spillways has been an important issue among hydraulic researchers for decades. In recent years, computing methods such as Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFISs) and Support Vector Regression (SVR) have shown a powerful performance in the prediction of scour characteristics owing to their flexibility and learning nature. In the present paper, a new hybrid approach has been proposed for the first time in order to improve the estimation power of the SVR tool for scour hole geometry prediction below ski-jump spillways. The principal characteristics of the scour hole pattern in the equilibrium phase have been predicted using SVR optimized with Fruitfly Optimization Algorithms (FOAs). The hybrid model is compared with the corresponding simple SVR model. To evaluate the proposed hybrid model further, it is also compared with other machine learning and empirical methods, such as ANNs, ANFISs and regression equations. The results show that the proposed SVR-FOA method performs well, improves remarkably on Support Vector Machines (SVMs) results, estimates scour hole geometrical parameters more accurately than the simple SVR model, and can be applied as an alternative reliable scheme for estimations on which simple SVR and other methods demonstrate shortcomings. The proposed hybrid method improves the precision level for scour depth prediction by about 8% compared with simple SVM in terms of the correlation coefficient.

Details

Original languageEnglish
Pages (from-to)272-291
Number of pages20
JournalEngineering applications of computational fluid mechanics
Volume15
Issue number1
Publication statusPublished - 2021
Peer-reviewedYes

Keywords

Keywords

  • artificial intelligence, artificial neural network (ANN), hydraulic model, Machine Learning, scour hole