Prediction of Hydraulic Jumps on a Triangular Bed Roughness Using Numerical Modeling and Soft Computing Methods
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Borrelli, A. & De Falco, I. & Della Cioppa, A. & Nicodemi, M. & Trautteur, G., 2006. "Performance of genetic programming to extract the trend in noisy data series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 104-108.
- Antoniadis, Anestis & Lambert-Lacroix, Sophie & Poggi, Jean-Michel, 2021. "Random forests for global sensitivity analysis: A selective review," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
- Pilowsky, Julia A. & Manica, Andrea & Brown, Stuart & Rahbek, Carsten & Fordham, Damien A., 2022. "Simulations of human migration into North America are more sensitive to demography than choice of palaeoclimate model," Ecological Modelling, Elsevier, vol. 473(C).
- Lei, Hongxuan & Liu, Pan & Cheng, Qian & Xu, Huan & Liu, Weibo & Zheng, Yalian & Chen, Xiangding & Zhou, Yong, 2024. "Frequency, duration, severity of energy drought and its propagation in hydro-wind-photovoltaic complementary systems," Renewable Energy, Elsevier, vol. 230(C).
- Ma, Yuan-Zhuo & Jin, Xiang-Xiang & Zhao, Xiang & Li, Hong-Shuang & Zhao, Zhen-Zhou & Xu, Chang, 2024. "Reliability-oriented global sensitivity analysis using subset simulation and space partition," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
- Hongquan Gui & Jialan Liu & Chi Ma & Mengyuan Li, 2024. "Industrial-oriented machine learning big data framework for temporal-spatial error prediction and control with DTSMGCN model," Journal of Intelligent Manufacturing, Springer, vol. 35(3), pages 1173-1196, March.
- Georgios Spanos & Antonios Lalas & Konstantinos Votis & Dimitrios Tzovaras, 2025. "Principal Component Random Forest for Passenger Demand Forecasting in Cooperative, Connected, and Automated Mobility," Sustainability, MDPI, vol. 17(6), pages 1-13, March.
- Gao, Zhikun & Yu, Junqi & Zhao, Anjun & Hu, Qun & Yang, Siyuan, 2022. "A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine," Energy, Elsevier, vol. 238(PC).
- Dela Rosa & Berna Elya & Muhammad Hanafi & Alfi Khatib & Eka Budiarto & Syamsu Nur & Muhammad Imam Surya, 2025. "Investigation of alpha-glucosidase inhibition activity of Artabotrys sumatranus leaf extract using metabolomics, machine learning and molecular docking analysis," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-32, January.
- Djandja, Oraléou Sangué & Salami, Adekunlé Akim & Wang, Zhi-Cong & Duo, Jia & Yin, Lin-Xin & Duan, Pei-Gao, 2022. "Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 245(C).
- Manuel Quintero & William T. Stephenson & Advik Shreekumar & Tamara Broderick, 2025. "Common Functional Decompositions Can Mis-attribute Differences in Outcomes Between Populations," Papers 2504.16864, arXiv.org.
- Xiong, Qingwen & Du, Peng & Deng, Jian & Huang, Daishun & Song, Gongle & Qian, Libo & Wu, Zenghui & Luo, Yuejian, 2022. "Global sensitivity analysis for nuclear reactor LBLOCA with time-dependent outputs," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
- Kévin Elie-Dit-Cosaque & Véronique Maume-Deschamps, 2024. "Random forest based quantile-oriented sensitivity analysis indices estimation," Computational Statistics, Springer, vol. 39(4), pages 1747-1777, June.
- Simsekler, Mecit Can Emre & Rodrigues, Clarence & Qazi, Abroon & Ellahham, Samer & Ozonoff, Al, 2021. "A comparative study of patient and staff safety evaluation using tree-based machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
- Zhang, Xiaodong & Dimitrov, Nikolay, 2024. "Variable importance analysis of wind turbine extreme responses with Shapley value explanation," Renewable Energy, Elsevier, vol. 232(C).
- Ling Tao & Yuanlai Xie & Chundong Hu, 2022. "Efficient Sensitivity Analysis for Enhanced Heat Transfer Performance of Heat Sink with Swirl Flow Structure under One-Side Heating," Energies, MDPI, vol. 15(19), pages 1-19, October.
- Kim, Jun Young & Kim, Dongjae & Li, Zezhong John & Dariva, Claudio & Cao, Yankai & Ellis, Naoko, 2023. "Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach," Energy, Elsevier, vol. 263(PC).
- Torii, André Jacomel & Novotny, Antonio André, 2021. "A priori error estimates for local reliability-based sensitivity analysis with Monte Carlo Simulation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
- Xiang Peng & Xiaoqing Xu & Jiquan Li & Shaofei Jiang, 2021. "A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters," Mathematics, MDPI, vol. 9(10), pages 1-18, May.
- Chen, Xuyong & Xu, Zhifeng & Wu, Yushun & Wu, Qiaoyun, 2023. "Heuristic algorithms for reliability estimation based on breadth-first search of a grid tree," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
- Run Zhou & Qing Gao & Qiuju Wang & Guoren Xu, 2025. "Machine Learning Optimization of Waste Salt Pyrolysis: Predicting Organic Pollutant Removal and Mass Loss," Sustainability, MDPI, vol. 17(7), pages 1-20, April.
More about this item
Keywords
artificial intelligence; energy dissipation; FLOW-3D; hydraulic jumps; bed roughness; sensitivity analysis;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3135-:d:695490. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.