Evidence map›Paper›PMID 38724562›Full record

ArticleScientific reports2024

A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration.

Bhupendra Joshi, Vijay Kumar Singh, Dinesh Kumar Vishwakarma, Mohammad Ali Ghorbani, Sungwon Kim, Shivam Gupta, V K Chandola, Jitendra Rajput, Il-Moon Chung, Krishna Kumar Yadav and 3 more

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Bhupendra JoshiDepartment of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India.
Vijay Kumar SinghDepartment of Soil and Water Conservation Engineering, Acharya Narendra Deva University of Agriculture & Technology, Kumarganj, Ayodhya, Uttar Pradesh, 224229, India.
Dinesh Kumar VishwakarmaDepartment of Irrigation and Drainage Engineering, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India. dinesh.vishwakarma4820@gmail.com.ORCID http://orcid.org/0000-0002-2421-6995
Mohammad Ali GhorbaniDepartment of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, 5166616471, Iran.ORCID http://orcid.org/0000-0002-0333-2559
Sungwon KimDepartment of Railroad Construction and Safety Engineering, Dongyang University, 36040, Yeongju, South Korea.ORCID http://orcid.org/0000-0002-9371-8884
Shivam GuptaDepartment of Irrigation and Drainage Engineering, Acharya Narendra Deva University of Agriculture & Technology, Kumarganj, Ayodhya, Uttar Pradesh, 224229, India.ORCID http://orcid.org/0000-0002-6026-1523
V K ChandolaDepartment of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India.
Jitendra RajputDivision of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.ORCID http://orcid.org/0000-0001-8299-9900
Il-Moon ChungDepartment of Water Resources and River Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si, 10223, Republic of Korea.ORCID http://orcid.org/0000-0003-0163-7305
Krishna Kumar YadavFaculty of Science and Technology, Madhyanchal Professional University, Ratibad, Bhopal, 462044, India.
Ehsan MirzaniaDepartment of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, 5166616471, Iran.ORCID http://orcid.org/0000-0001-9252-2241
Nadhir Al-AnsariDepartment of Civil, Environmental, and Natural Resources Engineering, Lulea University of Technology, 97187, Luleå, Sweden. nadhir.alansari@ltu.se.ORCID http://orcid.org/0000-0002-6790-2653
Mohamed A MattarDepartment of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh, 11451, Saudi Arabia. mmattar@ksu.edu.sa.ORCID http://orcid.org/0000-0002-7506-3036

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (N

Indexed as

Cascade correlation neural networkFeedforward neural networkMachine learningSeonath basinSuspended sediment concentration

Identifiers

PMID38724562
PMCPMC11082234

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