Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry-Reference-Cited by-同舟云学术

Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

Published:2019-01-01 Issue:1 Volume:13 Page:529-550
ISSN:1994-2060
Container-title:Engineering Applications of Computational Fluid Mechanics
language:en
Short-container-title:Engineering Applications of Computational Fluid Mechanics

Author:

Riahi-Madvar Hossien¹,Dehghani Majid²,Seifi Akram¹,Salwana Ely³,Shamshirband Shahaboddin⁴⁵,Mosavi Amir⁶⁷,Chau Kwok-wing⁸

Affiliation:

1. Department of Water Science & Engineering, Faculty of Agriculture, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

2. Technical and Engineering Department, Faculty of Civil Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

3. Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Malaysia

4. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

5. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

6. Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, Budapest-, Hungary

7. School of the Built Environment, Oxford Brookes University, Oxford, UK

8. Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, People’s Republic of China

Publisher

Informa UK Limited

Subject

Modelling and Simulation,General Computer Science

Link

https://tandfonline.com/doi/pdf/10.1080/19942060.2019.1618396

Reference69 articles.

1. Artificial Neural Networks in Hydrology. I: Preliminary Concepts

2. Azamathulla, H. M. (2005). Neural networks to estimate scour downstream of ski-jump bucket spillway [D] (Doctoral dissertation, Ph. D. Thesis). Indian Institute of Technology, Bombay.

3. Gene expression programming for prediction of scour depth downstream of sills

4. Estimation of pile group scour using adaptive neuro-fuzzy approach

5. Appraisal of soft computing techniques in prediction of total bed material load in tropical rivers

Cited by 40 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Application of Hybrid AI Models for Accurate Prediction of Scour Depths under Submerged Circular Vertical Jet;Journal of Hydrologic Engineering;2024-06

2. Predicting maximum scour depth uncertainty downstream of grade control structures: a multi-model approach with Bayesian model averaging;Journal of Applied Water Engineering and Research;2024-05-28

3. Improving the Reliability of Compound Channel Discharge Prediction Using Machine Learning Techniques and Resampling Methods;Water Resources Management;2024-05-26

4. Exploring the challenges and solutions for river scour in dynamic environments: a comprehensive review;World Journal of Engineering;2023-12-12

5. Yield prediction and optimization of biomass-based products by multi-machine learning schemes: Neural, regression and function-based techniques;Energy;2023-11