A novel problem-solving method by multi-computational optimisation of artificial neural network for modelling and prediction of the flow erosion processes

Moayedi, Hossein; Ahmadi Dehrashid, Atefeh; Nguyen Le, Binh

doi:10.1080/19942060.2023.2300456

Engineering Applications of Computational Fluid Mechanics

2024

DOI: 10.1080/19942060.2023.2300456

|View full text |Cite

A novel problem-solving method by multi-computational optimisation of artificial neural network for modelling and prediction of the flow erosion processes

Hossein Moayedi,

Atefeh Ahmadi Dehrashid,

Binh Nguyen Le

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 8 publications

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Modeling the total hardness (TH) of groundwater in aquifers using novel hybrid soft computing optimizer models

Moayedi,

Salari,

Ali

et al. 2024

Environ Earth Sci

View full text Add to dashboard Cite

Modeling the total hardness (TH) of groundwater in aquifers using novel hybrid soft computing optimizer models

Moayedi,

Salari,

Ali

et al. 2024

Environ Earth Sci

View full text Add to dashboard Cite

Analysis of long-term and short-term bankline stability prediction considering seasonal variations: A study in lower Ganga Basin, India

Asraful Alam,

Maiti

2024

Advances in Space Research

View full text Add to dashboard Cite

Novel embedding model predicting the credit card's default using neural network optimized by harmony search algorithm and vortex search algorithm

Xu,

2024

Heliyon

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A novel problem-solving method by multi-computational optimisation of artificial neural network for modelling and prediction of the flow erosion processes

Cited by 8 publications

References 72 publications

Modeling the total hardness (TH) of groundwater in aquifers using novel hybrid soft computing optimizer models

Modeling the total hardness (TH) of groundwater in aquifers using novel hybrid soft computing optimizer models

Analysis of long-term and short-term bankline stability prediction considering seasonal variations: A study in lower Ganga Basin, India

Novel embedding model predicting the credit card's default using neural network optimized by harmony search algorithm and vortex search algorithm

Contact Info

Product

Resources

About