A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration Article Swipe
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
,
Ehsan Mirzania
,
Nadhir Al‐Ansari
,
Mohamed A. Mattar
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-61339-1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-61339-1
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 ES ), root mean square error (RMSE), Willmott’s index of agreement (WI), and Legates–McCabe’s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Q t-n ) and suspended sediment concentration (S t-n ) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (S t-1 , S t-2 , S t-3 , S t-4 ) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest N ES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest N ES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-024-61339-1
- https://www.nature.com/articles/s41598-024-61339-1.pdf
- OA Status
- gold
- Cited By
- 28
- References
- 108
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396778459
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4396778459Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41598-024-61339-1Digital Object Identifier
- Title
-
A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentrationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-09Full publication date if available
- Authors
-
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, Ehsan Mirzania, Nadhir Al‐Ansari, Mohamed A. MattarList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-024-61339-1Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-024-61339-1.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/s41598-024-61339-1.pdfDirect OA link when available
- Concepts
-
Artificial neural network, Feedforward neural network, Computer science, Feed forward, Cascade, Sediment, Correlation, Artificial intelligence, Machine learning, Geology, Chemistry, Mathematics, Engineering, Control engineering, Chromatography, Paleontology, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
28Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 23, 2024: 5Per-year citation counts (last 5 years)
- References (count)
-
108Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4396778459 |
|---|---|
| doi | https://doi.org/10.1038/s41598-024-61339-1 |
| ids.doi | https://doi.org/10.1038/s41598-024-61339-1 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/38724562 |
| ids.openalex | https://openalex.org/W4396778459 |
| fwci | 10.84930326 |
| type | article |
| title | A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration |
| biblio.issue | 1 |
| biblio.volume | 14 |
| biblio.last_page | 10638 |
| biblio.first_page | 10638 |
| topics[0].id | https://openalex.org/T11490 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9995999932289124 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2305 |
| topics[0].subfield.display_name | Environmental Engineering |
| topics[0].display_name | Hydrological Forecasting Using AI |
| topics[1].id | https://openalex.org/T12697 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9858999848365784 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2312 |
| topics[1].subfield.display_name | Water Science and Technology |
| topics[1].display_name | Water Quality Monitoring Technologies |
| topics[2].id | https://openalex.org/T10320 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9797999858856201 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Neural Networks and Applications |
| is_xpac | False |
| apc_list.value | 1890 |
| apc_list.currency | EUR |
| apc_list.value_usd | 2190 |
| apc_paid.value | 1890 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 2190 |
| concepts[0].id | https://openalex.org/C50644808 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7813470363616943 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[0].display_name | Artificial neural network |
| concepts[1].id | https://openalex.org/C47702885 |
| concepts[1].level | 3 |
| concepts[1].score | 0.6972647905349731 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q5441227 |
| concepts[1].display_name | Feedforward neural network |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5424178242683411 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C38858127 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5322684049606323 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q5441228 |
| concepts[3].display_name | Feed forward |
| concepts[4].id | https://openalex.org/C34146451 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5136238932609558 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q5048094 |
| concepts[4].display_name | Cascade |
| concepts[5].id | https://openalex.org/C2816523 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4822707772254944 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q180184 |
| concepts[5].display_name | Sediment |
| concepts[6].id | https://openalex.org/C117220453 |
| concepts[6].level | 2 |
| concepts[6].score | 0.47950679063796997 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q5172842 |
| concepts[6].display_name | Correlation |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.43775075674057007 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.42417973279953003 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C127313418 |
| concepts[9].level | 0 |
| concepts[9].score | 0.12451040744781494 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[9].display_name | Geology |
| concepts[10].id | https://openalex.org/C185592680 |
| concepts[10].level | 0 |
| concepts[10].score | 0.11844134330749512 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[10].display_name | Chemistry |
| concepts[11].id | https://openalex.org/C33923547 |
| concepts[11].level | 0 |
| concepts[11].score | 0.10166260600090027 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[11].display_name | Mathematics |
| concepts[12].id | https://openalex.org/C127413603 |
| concepts[12].level | 0 |
| concepts[12].score | 0.06496110558509827 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[12].display_name | Engineering |
| concepts[13].id | https://openalex.org/C133731056 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q4917288 |
| concepts[13].display_name | Control engineering |
| concepts[14].id | https://openalex.org/C43617362 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q170050 |
| concepts[14].display_name | Chromatography |
| concepts[15].id | https://openalex.org/C151730666 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[15].display_name | Paleontology |
| concepts[16].id | https://openalex.org/C2524010 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[16].display_name | Geometry |
| keywords[0].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[0].score | 0.7813470363616943 |
| keywords[0].display_name | Artificial neural network |
| keywords[1].id | https://openalex.org/keywords/feedforward-neural-network |
| keywords[1].score | 0.6972647905349731 |
| keywords[1].display_name | Feedforward neural network |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.5424178242683411 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/feed-forward |
| keywords[3].score | 0.5322684049606323 |
| keywords[3].display_name | Feed forward |
| keywords[4].id | https://openalex.org/keywords/cascade |
| keywords[4].score | 0.5136238932609558 |
| keywords[4].display_name | Cascade |
| keywords[5].id | https://openalex.org/keywords/sediment |
| keywords[5].score | 0.4822707772254944 |
| keywords[5].display_name | Sediment |
| keywords[6].id | https://openalex.org/keywords/correlation |
| keywords[6].score | 0.47950679063796997 |
| keywords[6].display_name | Correlation |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.43775075674057007 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.42417973279953003 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/geology |
| keywords[9].score | 0.12451040744781494 |
| keywords[9].display_name | Geology |
| keywords[10].id | https://openalex.org/keywords/chemistry |
| keywords[10].score | 0.11844134330749512 |
| keywords[10].display_name | Chemistry |
| keywords[11].id | https://openalex.org/keywords/mathematics |
| keywords[11].score | 0.10166260600090027 |
| keywords[11].display_name | Mathematics |
| keywords[12].id | https://openalex.org/keywords/engineering |
| keywords[12].score | 0.06496110558509827 |
| keywords[12].display_name | Engineering |
| language | en |
| locations[0].id | doi:10.1038/s41598-024-61339-1 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S196734849 |
| locations[0].source.issn | 2045-2322 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2045-2322 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Scientific Reports |
| locations[0].source.host_organization | https://openalex.org/P4310319908 |
| locations[0].source.host_organization_name | Nature Portfolio |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.nature.com/articles/s41598-024-61339-1.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Scientific Reports |
| locations[0].landing_page_url | https://doi.org/10.1038/s41598-024-61339-1 |
| locations[1].id | pmid:38724562 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Scientific reports |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/38724562 |
| locations[2].id | pmh:oai:DiVA.org:ltu-105434 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400013 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | Publications (Konstfack University of Arts, Crafts, and Design) |
| locations[2].source.host_organization | https://openalex.org/I218177685 |
| locations[2].source.host_organization_name | University College of Arts Crafts and Design |
| locations[2].source.host_organization_lineage | https://openalex.org/I218177685 |
| locations[2].license | other-oa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Article in journal |
| locations[2].license_id | https://openalex.org/licenses/other-oa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-105434 |
| locations[3].id | pmh:oai:doaj.org/article:49768b418f73407fad57c9232992b6d6 |
| locations[3].is_oa | False |
| locations[3].source.id | https://openalex.org/S4306401280 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[3].source.host_organization | |
| locations[3].source.host_organization_name | |
| locations[3].license | |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | article |
| locations[3].license_id | |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024) |
| locations[3].landing_page_url | https://doaj.org/article/49768b418f73407fad57c9232992b6d6 |
| locations[4].id | pmh:oai:pubmedcentral.nih.gov:11082234 |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S2764455111 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | False |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | PubMed Central |
| locations[4].source.host_organization | https://openalex.org/I1299303238 |
| locations[4].source.host_organization_name | National Institutes of Health |
| locations[4].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[4].license | other-oa |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | Text |
| locations[4].license_id | https://openalex.org/licenses/other-oa |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Sci Rep |
| locations[4].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11082234 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5102662435 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Bhupendra Joshi |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I91357014 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India |
| authorships[0].institutions[0].id | https://openalex.org/I91357014 |
| authorships[0].institutions[0].ror | https://ror.org/04cdn2797 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I91357014 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Banaras Hindu University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Bhupendra Joshi |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India |
| authorships[1].author.id | https://openalex.org/A5062604463 |
| authorships[1].author.orcid | https://orcid.org/0009-0004-6072-2918 |
| authorships[1].author.display_name | Vijay Kumar Singh |
| authorships[1].countries | IN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210097692 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Soil and Water Conservation Engineering, Acharya Narendra Deva University of Agriculture & Technology, Kumarganj, Ayodhya, Uttar Pradesh, 224229, India |
| authorships[1].institutions[0].id | https://openalex.org/I4210097692 |
| authorships[1].institutions[0].ror | https://ror.org/00zw5g161 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210097692 |
| authorships[1].institutions[0].country_code | IN |
| authorships[1].institutions[0].display_name | Acharya Narendra Deva University of Agriculture and Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Vijay Kumar Singh |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Soil and Water Conservation Engineering, Acharya Narendra Deva University of Agriculture & Technology, Kumarganj, Ayodhya, Uttar Pradesh, 224229, India |
| authorships[2].author.id | https://openalex.org/A5066816551 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-2421-6995 |
| authorships[2].author.display_name | Dinesh Kumar Vishwakarma |
| authorships[2].countries | IN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I252758333 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Irrigation and Drainage Engineering, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India |
| authorships[2].institutions[0].id | https://openalex.org/I252758333 |
| authorships[2].institutions[0].ror | https://ror.org/02msjvh03 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I252758333 |
| authorships[2].institutions[0].country_code | IN |
| authorships[2].institutions[0].display_name | Govind Ballabh Pant University of Agriculture and Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Dinesh Kumar Vishwakarma |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | Department of Irrigation and Drainage Engineering, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India |
| authorships[3].author.id | https://openalex.org/A5101909652 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-0333-2559 |
| authorships[3].author.display_name | Mohammad Ali Ghorbani |
| authorships[3].countries | IR |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I41832843 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, 5166616471, Iran |
| authorships[3].institutions[0].id | https://openalex.org/I41832843 |
| authorships[3].institutions[0].ror | https://ror.org/01papkj44 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I41832843 |
| authorships[3].institutions[0].country_code | IR |
| authorships[3].institutions[0].display_name | University of Tabriz |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Mohammad Ali Ghorbani |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, 5166616471, Iran |
| authorships[4].author.id | https://openalex.org/A5100327702 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-9371-8884 |
| authorships[4].author.display_name | Sungwon Kim |
| authorships[4].countries | KR |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I201873833 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Railroad Construction and Safety Engineering, Dongyang University, 36040, Yeongju, South Korea |
| authorships[4].institutions[0].id | https://openalex.org/I201873833 |
| authorships[4].institutions[0].ror | https://ror.org/05v1ekw79 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I201873833 |
| authorships[4].institutions[0].country_code | KR |
| authorships[4].institutions[0].display_name | Dongyang University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Sungwon Kim |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Railroad Construction and Safety Engineering, Dongyang University, 36040, Yeongju, South Korea |
| authorships[5].author.id | https://openalex.org/A5101757708 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Shivam Gupta |
| authorships[5].countries | IN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I4210097692 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Irrigation and Drainage Engineering, Acharya Narendra Deva University of Agriculture & Technology, Kumarganj, Ayodhya, Uttar Pradesh, 224229, India |
| authorships[5].institutions[0].id | https://openalex.org/I4210097692 |
| authorships[5].institutions[0].ror | https://ror.org/00zw5g161 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I4210097692 |
| authorships[5].institutions[0].country_code | IN |
| authorships[5].institutions[0].display_name | Acharya Narendra Deva University of Agriculture and Technology |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Shivam Gupta |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Irrigation and Drainage Engineering, Acharya Narendra Deva University of Agriculture & Technology, Kumarganj, Ayodhya, Uttar Pradesh, 224229, India |
| authorships[6].author.id | https://openalex.org/A5015941402 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | V. K. Chandola |
| authorships[6].countries | IN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I91357014 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India |
| authorships[6].institutions[0].id | https://openalex.org/I91357014 |
| authorships[6].institutions[0].ror | https://ror.org/04cdn2797 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I91357014 |
| authorships[6].institutions[0].country_code | IN |
| authorships[6].institutions[0].display_name | Banaras Hindu University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | V. K. Chandola |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Department of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India |
| authorships[7].author.id | https://openalex.org/A5016049562 |
| authorships[7].author.orcid | https://orcid.org/0000-0001-8299-9900 |
| authorships[7].author.display_name | Jitendra Rajput |
| authorships[7].countries | IN |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I45509622 |
| authorships[7].affiliations[0].raw_affiliation_string | Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India |
| authorships[7].institutions[0].id | https://openalex.org/I45509622 |
| authorships[7].institutions[0].ror | https://ror.org/01bzgdw81 |
| authorships[7].institutions[0].type | facility |
| authorships[7].institutions[0].lineage | https://openalex.org/I179420787, https://openalex.org/I45509622 |
| authorships[7].institutions[0].country_code | IN |
| authorships[7].institutions[0].display_name | Indian Agricultural Research Institute |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Jitendra Rajput |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India |
| authorships[8].author.id | https://openalex.org/A5058512457 |
| authorships[8].author.orcid | https://orcid.org/0000-0003-0163-7305 |
| authorships[8].author.display_name | Il-Moon Chung |
| authorships[8].countries | KR |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I3019177047 |
| authorships[8].affiliations[0].raw_affiliation_string | Department of Water Resources and River Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si, 10223, Republic of Korea |
| authorships[8].institutions[0].id | https://openalex.org/I3019177047 |
| authorships[8].institutions[0].ror | https://ror.org/035enhp47 |
| authorships[8].institutions[0].type | government |
| authorships[8].institutions[0].lineage | https://openalex.org/I3019177047 |
| authorships[8].institutions[0].country_code | KR |
| authorships[8].institutions[0].display_name | Korea Institute of Civil Engineering and Building Technology |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Il-Moon Chung |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Department of Water Resources and River Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si, 10223, Republic of Korea |
| authorships[9].author.id | https://openalex.org/A5023613989 |
| authorships[9].author.orcid | https://orcid.org/0000-0002-4228-2726 |
| authorships[9].author.display_name | Krishna Kumar Yadav |
| authorships[9].affiliations[0].raw_affiliation_string | Faculty of Science and Technology, Madhyanchal Professional University, Ratibad, Bhopal, 462044, India |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Krishna Kumar Yadav |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | Faculty of Science and Technology, Madhyanchal Professional University, Ratibad, Bhopal, 462044, India |
| authorships[10].author.id | https://openalex.org/A5034901435 |
| authorships[10].author.orcid | https://orcid.org/0000-0001-9252-2241 |
| authorships[10].author.display_name | Ehsan Mirzania |
| authorships[10].countries | IR |
| authorships[10].affiliations[0].institution_ids | https://openalex.org/I41832843 |
| authorships[10].affiliations[0].raw_affiliation_string | Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, 5166616471, Iran |
| authorships[10].institutions[0].id | https://openalex.org/I41832843 |
| authorships[10].institutions[0].ror | https://ror.org/01papkj44 |
| authorships[10].institutions[0].type | education |
| authorships[10].institutions[0].lineage | https://openalex.org/I41832843 |
| authorships[10].institutions[0].country_code | IR |
| authorships[10].institutions[0].display_name | University of Tabriz |
| authorships[10].author_position | middle |
| authorships[10].raw_author_name | Ehsan Mirzania |
| authorships[10].is_corresponding | False |
| authorships[10].raw_affiliation_strings | Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, 5166616471, Iran |
| authorships[11].author.id | https://openalex.org/A5070829177 |
| authorships[11].author.orcid | https://orcid.org/0000-0002-6790-2653 |
| authorships[11].author.display_name | Nadhir Al‐Ansari |
| authorships[11].countries | SE |
| authorships[11].affiliations[0].institution_ids | https://openalex.org/I190632392 |
| authorships[11].affiliations[0].raw_affiliation_string | Department of Civil, Environmental, and Natural Resources Engineering, Lulea University of Technology, 97187, Luleå, Sweden |
| authorships[11].institutions[0].id | https://openalex.org/I190632392 |
| authorships[11].institutions[0].ror | https://ror.org/016st3p78 |
| authorships[11].institutions[0].type | education |
| authorships[11].institutions[0].lineage | https://openalex.org/I190632392 |
| authorships[11].institutions[0].country_code | SE |
| authorships[11].institutions[0].display_name | Luleå University of Technology |
| authorships[11].author_position | middle |
| authorships[11].raw_author_name | Nadhir Al-Ansari |
| authorships[11].is_corresponding | True |
| authorships[11].raw_affiliation_strings | Department of Civil, Environmental, and Natural Resources Engineering, Lulea University of Technology, 97187, Luleå, Sweden |
| authorships[12].author.id | https://openalex.org/A5034772572 |
| authorships[12].author.orcid | https://orcid.org/0000-0002-7506-3036 |
| authorships[12].author.display_name | Mohamed A. Mattar |
| authorships[12].countries | SA |
| authorships[12].affiliations[0].institution_ids | https://openalex.org/I28022161 |
| authorships[12].affiliations[0].raw_affiliation_string | Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh, 11451, Saudi Arabia |
| authorships[12].institutions[0].id | https://openalex.org/I28022161 |
| authorships[12].institutions[0].ror | https://ror.org/02f81g417 |
| authorships[12].institutions[0].type | education |
| authorships[12].institutions[0].lineage | https://openalex.org/I28022161 |
| authorships[12].institutions[0].country_code | SA |
| authorships[12].institutions[0].display_name | King Saud University |
| authorships[12].author_position | last |
| authorships[12].raw_author_name | Mohamed A. Mattar |
| authorships[12].is_corresponding | True |
| authorships[12].raw_affiliation_strings | Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh, 11451, Saudi Arabia |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.nature.com/articles/s41598-024-61339-1.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11490 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9995999932289124 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2305 |
| primary_topic.subfield.display_name | Environmental Engineering |
| primary_topic.display_name | Hydrological Forecasting Using AI |
| related_works | https://openalex.org/W2153719181, https://openalex.org/W1971748923, https://openalex.org/W1566155057, https://openalex.org/W2115072676, https://openalex.org/W4311212821, https://openalex.org/W2045727192, https://openalex.org/W1529660427, https://openalex.org/W2102065768, https://openalex.org/W4390752998, https://openalex.org/W2158578859 |
| cited_by_count | 28 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 23 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 5 |
| locations_count | 5 |
| best_oa_location.id | doi:10.1038/s41598-024-61339-1 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S196734849 |
| best_oa_location.source.issn | 2045-2322 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2045-2322 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Scientific Reports |
| best_oa_location.source.host_organization | https://openalex.org/P4310319908 |
| best_oa_location.source.host_organization_name | Nature Portfolio |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.nature.com/articles/s41598-024-61339-1.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Scientific Reports |
| best_oa_location.landing_page_url | https://doi.org/10.1038/s41598-024-61339-1 |
| primary_location.id | doi:10.1038/s41598-024-61339-1 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S196734849 |
| primary_location.source.issn | 2045-2322 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2045-2322 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Scientific Reports |
| primary_location.source.host_organization | https://openalex.org/P4310319908 |
| primary_location.source.host_organization_name | Nature Portfolio |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.nature.com/articles/s41598-024-61339-1.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Scientific Reports |
| primary_location.landing_page_url | https://doi.org/10.1038/s41598-024-61339-1 |
| publication_date | 2024-05-09 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W1923650576, https://openalex.org/W197565628, https://openalex.org/W4383999500, https://openalex.org/W3163085940, https://openalex.org/W3146795850, https://openalex.org/W2039165444, https://openalex.org/W2000008544, https://openalex.org/W2996070409, https://openalex.org/W2147377505, https://openalex.org/W2189249735, https://openalex.org/W2167493718, https://openalex.org/W2073430640, https://openalex.org/W2616745531, https://openalex.org/W3216740510, https://openalex.org/W4210866805, https://openalex.org/W3081693694, https://openalex.org/W2008661373, https://openalex.org/W4214620440, https://openalex.org/W3097879628, https://openalex.org/W2608079944, https://openalex.org/W2074346324, https://openalex.org/W2895846926, https://openalex.org/W2284731747, https://openalex.org/W2142775068, https://openalex.org/W2029724692, https://openalex.org/W2036589008, https://openalex.org/W2160897371, https://openalex.org/W2147630087, https://openalex.org/W2745671103, https://openalex.org/W2756144590, https://openalex.org/W2952404835, https://openalex.org/W4387458750, https://openalex.org/W4367550560, https://openalex.org/W4297965347, https://openalex.org/W3091593743, https://openalex.org/W2807276156, https://openalex.org/W2104609361, https://openalex.org/W2737693579, https://openalex.org/W3159598116, https://openalex.org/W2806072166, https://openalex.org/W2897565907, https://openalex.org/W2900233077, https://openalex.org/W2783448417, https://openalex.org/W3042464261, https://openalex.org/W2002351729, https://openalex.org/W4385075252, https://openalex.org/W4362665493, https://openalex.org/W4391820614, https://openalex.org/W4391722817, https://openalex.org/W3132527957, https://openalex.org/W4293774972, https://openalex.org/W3216804127, https://openalex.org/W4226212353, https://openalex.org/W4206843724, https://openalex.org/W4225115494, https://openalex.org/W4285271147, https://openalex.org/W4379356247, https://openalex.org/W2024520223, https://openalex.org/W2017287733, https://openalex.org/W2999127782, https://openalex.org/W2952538340, https://openalex.org/W1966671549, https://openalex.org/W2066024482, https://openalex.org/W2026302091, https://openalex.org/W2977306776, https://openalex.org/W6768394360, https://openalex.org/W2977278354, https://openalex.org/W4301040933, https://openalex.org/W2907283755, https://openalex.org/W2982507217, https://openalex.org/W1986760892, https://openalex.org/W2302101267, https://openalex.org/W1740585449, https://openalex.org/W2023430894, https://openalex.org/W3091468550, https://openalex.org/W2041534329, https://openalex.org/W2541011702, https://openalex.org/W2137983211, https://openalex.org/W2944532009, https://openalex.org/W3003717755, https://openalex.org/W2978294269, https://openalex.org/W2810788202, https://openalex.org/W2033904036, https://openalex.org/W3000069561, https://openalex.org/W3018490167, https://openalex.org/W1481169268, https://openalex.org/W2977074346, https://openalex.org/W2037460094, https://openalex.org/W4200073531, https://openalex.org/W4389289183, https://openalex.org/W4307647884, https://openalex.org/W4386545471, https://openalex.org/W4376609385, https://openalex.org/W4364356987, https://openalex.org/W4323669865, https://openalex.org/W4226340923, https://openalex.org/W3198482670, https://openalex.org/W4311798475, https://openalex.org/W4200425393, https://openalex.org/W1983212802, https://openalex.org/W4220890252, https://openalex.org/W2327083007, https://openalex.org/W2003623399, https://openalex.org/W2061435454, https://openalex.org/W3037312783, https://openalex.org/W4317209776, https://openalex.org/W4284898335, https://openalex.org/W3119273299 |
| referenced_works_count | 108 |
| abstract_inverted_index.) | 153, 160, 198 |
| abstract_inverted_index., | 189, 192, 195 |
| abstract_inverted_index.4 | 184 |
| abstract_inverted_index.= | 208, 216, 219, 223, 242, 250, 253, 257 |
| abstract_inverted_index.N | 214, 248 |
| abstract_inverted_index.S | 190, 193, 196 |
| abstract_inverted_index.a | 118 |
| abstract_inverted_index.(N | 99 |
| abstract_inverted_index.(Q | 151 |
| abstract_inverted_index.(S | 158, 187 |
| abstract_inverted_index.), | 101 |
| abstract_inverted_index.ES | 100, 215, 249 |
| abstract_inverted_index.In | 26 |
| abstract_inverted_index.LM | 222, 256 |
| abstract_inverted_index.WI | 218, 252 |
| abstract_inverted_index.as | 161, 170, 235 |
| abstract_inverted_index.at | 53, 279, 300 |
| abstract_inverted_index.by | 117 |
| abstract_inverted_index.in | 58, 282 |
| abstract_inverted_index.is | 5 |
| abstract_inverted_index.of | 10, 17, 109 |
| abstract_inverted_index.to | 47, 70, 76, 80, 174, 298 |
| abstract_inverted_index.The | 85, 130, 180, 263 |
| abstract_inverted_index.and | 23, 40, 55, 65, 74, 95, 112, 124, 135, 139, 154, 177, 211, 221, 245, 255 |
| abstract_inverted_index.for | 7, 127, 149, 225, 259, 275, 293, 306 |
| abstract_inverted_index.t-1 | 188 |
| abstract_inverted_index.t-2 | 191 |
| abstract_inverted_index.t-3 | 194 |
| abstract_inverted_index.t-4 | 197 |
| abstract_inverted_index.t-n | 152, 159 |
| abstract_inverted_index.the | 8, 34, 78, 165, 171, 200, 205, 212, 226, 230, 236, 239, 246, 260, 266, 272, 283 |
| abstract_inverted_index.two | 29 |
| abstract_inverted_index.was | 133, 269 |
| abstract_inverted_index.2010 | 69 |
| abstract_inverted_index.2015 | 71 |
| abstract_inverted_index.CCNN | 138, 176, 267, 288 |
| abstract_inverted_index.FFNN | 178, 273, 299 |
| abstract_inverted_index.Nash | 94 |
| abstract_inverted_index.Nine | 141 |
| abstract_inverted_index.RMSE | 207, 241 |
| abstract_inverted_index.best | 237 |
| abstract_inverted_index.both | 280, 301 |
| abstract_inverted_index.data | 67 |
| abstract_inverted_index.from | 68 |
| abstract_inverted_index.like | 93 |
| abstract_inverted_index.mean | 103 |
| abstract_inverted_index.mg/l | 210, 244 |
| abstract_inverted_index.root | 102 |
| abstract_inverted_index.same | 231 |
| abstract_inverted_index.than | 271 |
| abstract_inverted_index.this | 27 |
| abstract_inverted_index.used | 75 |
| abstract_inverted_index.were | 45, 72, 88, 144 |
| abstract_inverted_index.with | 137, 164, 183, 204, 238, 309 |
| abstract_inverted_index.(LM), | 115 |
| abstract_inverted_index.(SSC) | 52 |
| abstract_inverted_index.(WI), | 111 |
| abstract_inverted_index.0.668 | 224 |
| abstract_inverted_index.0.788 | 258 |
| abstract_inverted_index.0.890 | 220 |
| abstract_inverted_index.0.936 | 254 |
| abstract_inverted_index.53.71 | 243 |
| abstract_inverted_index.95.02 | 209 |
| abstract_inverted_index.CCNN4 | 181, 232 |
| abstract_inverted_index.FFNN. | 140 |
| abstract_inverted_index.Simga | 54, 261 |
| abstract_inverted_index.dams, | 12 |
| abstract_inverted_index.error | 105 |
| abstract_inverted_index.index | 108, 114 |
| abstract_inverted_index.input | 142, 162 |
| abstract_inverted_index.model | 79, 132, 182, 233, 268, 274 |
| abstract_inverted_index.other | 201 |
| abstract_inverted_index.plot, | 120 |
| abstract_inverted_index.shows | 265 |
| abstract_inverted_index.their | 304 |
| abstract_inverted_index.using | 90, 146 |
| abstract_inverted_index.water | 24 |
| abstract_inverted_index.while | 229 |
| abstract_inverted_index.(CCNN) | 39 |
| abstract_inverted_index.(FFNN) | 44 |
| abstract_inverted_index.0.785, | 251 |
| abstract_inverted_index.India. | 61, 286 |
| abstract_inverted_index.Taylor | 125 |
| abstract_inverted_index.basin, | 60, 285 |
| abstract_inverted_index.better | 270, 290 |
| abstract_inverted_index.design | 9 |
| abstract_inverted_index.inputs | 186 |
| abstract_inverted_index.lagged | 185 |
| abstract_inverted_index.lowest | 206, 240 |
| abstract_inverted_index.models | 87, 203 |
| abstract_inverted_index.namely | 33 |
| abstract_inverted_index.neural | 37, 42 |
| abstract_inverted_index.plots, | 122 |
| abstract_inverted_index.result | 264 |
| abstract_inverted_index.rivers | 13 |
| abstract_inverted_index.secure | 234 |
| abstract_inverted_index.showed | 289 |
| abstract_inverted_index.square | 104 |
| abstract_inverted_index.study, | 28 |
| abstract_inverted_index.(RMSE), | 106 |
| abstract_inverted_index.Station | 228 |
| abstract_inverted_index.applied | 46 |
| abstract_inverted_index.aquatic | 18 |
| abstract_inverted_index.cascade | 35 |
| abstract_inverted_index.complex | 310 |
| abstract_inverted_index.current | 166 |
| abstract_inverted_index.density | 121 |
| abstract_inverted_index.desired | 172 |
| abstract_inverted_index.develop | 77, 175 |
| abstract_inverted_index.diagram | 126 |
| abstract_inverted_index.highest | 213, 247 |
| abstract_inverted_index.indices | 92 |
| abstract_inverted_index.machine | 31 |
| abstract_inverted_index.models, | 32 |
| abstract_inverted_index.models. | 179 |
| abstract_inverted_index.network | 38, 43 |
| abstract_inverted_index.output, | 173 |
| abstract_inverted_index.predict | 48, 81 |
| abstract_inverted_index.safety, | 22 |
| abstract_inverted_index.scatter | 119 |
| abstract_inverted_index.various | 15 |
| abstract_inverted_index.0.0.662, | 217 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Jondhara | 56, 227 |
| abstract_inverted_index.Overall, | 287 |
| abstract_inverted_index.Sheonath | 59, 284 |
| abstract_inverted_index.Station. | 262 |
| abstract_inverted_index.compared | 136, 297 |
| abstract_inverted_index.critical | 6 |
| abstract_inverted_index.explored | 145 |
| abstract_inverted_index.resource | 19 |
| abstract_inverted_index.sediment | 2, 50, 63, 83, 156, 168, 278, 295 |
| abstract_inverted_index.stations | 57, 281 |
| abstract_inverted_index.Suspended | 1 |
| abstract_inverted_index.Sutcliffe | 96 |
| abstract_inverted_index.agreement | 110 |
| abstract_inverted_index.collected | 73 |
| abstract_inverted_index.developed | 86, 131, 202 |
| abstract_inverted_index.different | 30, 147 |
| abstract_inverted_index.discharge | 66, 150 |
| abstract_inverted_index.evaluated | 89, 134 |
| abstract_inverted_index.graphical | 128 |
| abstract_inverted_index.lag-times | 148 |
| abstract_inverted_index.potential | 292 |
| abstract_inverted_index.stations, | 302 |
| abstract_inverted_index.suspended | 82, 155, 167, 294 |
| abstract_inverted_index.efficiency | 97 |
| abstract_inverted_index.histograms | 123 |
| abstract_inverted_index.operations | 16 |
| abstract_inverted_index.predicting | 276 |
| abstract_inverted_index.prediction | 4 |
| abstract_inverted_index.structure, | 20 |
| abstract_inverted_index.variables, | 163 |
| abstract_inverted_index.coefficient | 98 |
| abstract_inverted_index.correlation | 36 |
| abstract_inverted_index.ecosystems, | 14 |
| abstract_inverted_index.feedforward | 41 |
| abstract_inverted_index.forecasting | 291, 308 |
| abstract_inverted_index.management. | 25 |
| abstract_inverted_index.reservoirs, | 11 |
| abstract_inverted_index.statistical | 91 |
| abstract_inverted_index.Willmott’s | 107 |
| abstract_inverted_index.combinations | 143 |
| abstract_inverted_index.hydrological | 307 |
| abstract_inverted_index.outperformed | 199 |
| abstract_inverted_index.supplemented | 116 |
| abstract_inverted_index.applicability | 305 |
| abstract_inverted_index.concentration | 3, 51, 64, 157, 169, 296 |
| abstract_inverted_index.demonstrating | 303 |
| abstract_inverted_index.environmental | 21 |
| abstract_inverted_index.concentration. | 84 |
| abstract_inverted_index.relationships. | 311 |
| abstract_inverted_index.Daily-suspended | 62 |
| abstract_inverted_index.daily-suspended | 49, 277 |
| abstract_inverted_index.representation. | 129 |
| abstract_inverted_index.Legates–McCabe’s | 113 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5034772572, https://openalex.org/A5070829177, https://openalex.org/A5066816551 |
| countries_distinct_count | 5 |
| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I190632392, https://openalex.org/I252758333, https://openalex.org/I28022161 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.6299999952316284 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.98390522 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |