Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6 Article Swipe
Hamid Anwar
,
Afed Ullah Khan
,
Basir Ullah
,
Abubakr Taha Bakheit Taha
,
Taoufik Najeh
,
Muhammad Usman Badshah
,
Abdulnoor A. J. Ghanim
,
M. Irfan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-63989-7
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-63989-7
This research was carried out to predict daily streamflow for the Swat River Basin, Pakistan through four deep learning (DL) models: Feed Forward Artificial Neural Networks (FFANN), Seasonal Artificial Neural Networks (SANN), Time Lag Artificial Neural Networks (TLANN) and Long Short-Term Memory (LSTM) under two Shared Socioeconomic Pathways (SSPs) 585 and 245. Taylor Diagram, Random Forest, and Gradient Boosting techniques were used to select the best combination of General Circulation Models (GCMs) for Multi-Model Ensemble (MME) computation. MME was computed via the Random Forest technique for Maximum Temperature (Tmax), Minimum Temperature (Tmin), and precipitation for the aforementioned three techniques. The best MME for Tmax, Tmin, and precipitation was rendered by Compromise Programming. The DL models were trained and tested using observed precipitation and temperature as independent variables and discharge as dependent variables. The results of deep learning models were evaluated using statistical performance indicators such as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). The TLANN demonstrated superior performance compared to the other models based on RMSE, MSE, MAE, and R2 during training (65.25 m3/s, 4256.97 m3/s, 46.793 m3/s and 0.7978) and testing (72.06 m3/s, 5192.95 m3/s, 51.363 m3/s and 0.7443) respectively. Subsequently, TLANN was utilized to make predictions based on MME of SSP245 and SSP585 scenarios for future streamflow until the year 2100. These results can be used for planning, management, and policy-making regarding water resources projects in the study area.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-024-63989-7
- https://www.nature.com/articles/s41598-024-63989-7.pdf
- OA Status
- gold
- Cited By
- 12
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401150769
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4401150769Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41598-024-63989-7Digital Object Identifier
- Title
-
Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-29Full publication date if available
- Authors
-
Hamid Anwar, Afed Ullah Khan, Basir Ullah, Abubakr Taha Bakheit Taha, Taoufik Najeh, Muhammad Usman Badshah, Abdulnoor A. J. Ghanim, M. IrfanList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-024-63989-7Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-024-63989-7.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/s41598-024-63989-7.pdfDirect OA link when available
- Concepts
-
Streamflow, Computer science, Data science, Machine learning, Cartography, Geography, Drainage basinTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 11, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4401150769 |
|---|---|
| doi | https://doi.org/10.1038/s41598-024-63989-7 |
| ids.doi | https://doi.org/10.1038/s41598-024-63989-7 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39080322 |
| ids.openalex | https://openalex.org/W4401150769 |
| fwci | 4.76327774 |
| type | article |
| title | Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6 |
| biblio.issue | 1 |
| biblio.volume | 14 |
| biblio.last_page | 17468 |
| biblio.first_page | 17468 |
| topics[0].id | https://openalex.org/T10330 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9994999766349792 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2312 |
| topics[0].subfield.display_name | Water Science and Technology |
| topics[0].display_name | Hydrology and Watershed Management Studies |
| topics[1].id | https://openalex.org/T10466 |
| topics[1].field.id | https://openalex.org/fields/19 |
| topics[1].field.display_name | Earth and Planetary Sciences |
| topics[1].score | 0.9991999864578247 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1902 |
| topics[1].subfield.display_name | Atmospheric Science |
| topics[1].display_name | Meteorological Phenomena and Simulations |
| topics[2].id | https://openalex.org/T11490 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9990000128746033 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2305 |
| topics[2].subfield.display_name | Environmental Engineering |
| topics[2].display_name | Hydrological Forecasting Using AI |
| 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/C53739315 |
| concepts[0].level | 3 |
| concepts[0].score | 0.702668309211731 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q29425295 |
| concepts[0].display_name | Streamflow |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5060846209526062 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C2522767166 |
| concepts[2].level | 1 |
| concepts[2].score | 0.33923566341400146 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[2].display_name | Data science |
| concepts[3].id | https://openalex.org/C119857082 |
| concepts[3].level | 1 |
| concepts[3].score | 0.3321858048439026 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[3].display_name | Machine learning |
| concepts[4].id | https://openalex.org/C58640448 |
| concepts[4].level | 1 |
| concepts[4].score | 0.09061139822006226 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q42515 |
| concepts[4].display_name | Cartography |
| concepts[5].id | https://openalex.org/C205649164 |
| concepts[5].level | 0 |
| concepts[5].score | 0.08627131581306458 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[5].display_name | Geography |
| concepts[6].id | https://openalex.org/C126645576 |
| concepts[6].level | 2 |
| concepts[6].score | 0.0 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q166620 |
| concepts[6].display_name | Drainage basin |
| keywords[0].id | https://openalex.org/keywords/streamflow |
| keywords[0].score | 0.702668309211731 |
| keywords[0].display_name | Streamflow |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5060846209526062 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/data-science |
| keywords[2].score | 0.33923566341400146 |
| keywords[2].display_name | Data science |
| keywords[3].id | https://openalex.org/keywords/machine-learning |
| keywords[3].score | 0.3321858048439026 |
| keywords[3].display_name | Machine learning |
| keywords[4].id | https://openalex.org/keywords/cartography |
| keywords[4].score | 0.09061139822006226 |
| keywords[4].display_name | Cartography |
| keywords[5].id | https://openalex.org/keywords/geography |
| keywords[5].score | 0.08627131581306458 |
| keywords[5].display_name | Geography |
| language | en |
| locations[0].id | doi:10.1038/s41598-024-63989-7 |
| 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-nc-nd |
| locations[0].pdf_url | https://www.nature.com/articles/s41598-024-63989-7.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| 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/s41598-024-63989-7 |
| locations[1].id | pmid:39080322 |
| 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/39080322 |
| locations[2].id | pmh:oai:DiVA.org:ltu-108471 |
| 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 | text |
| 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-108471 |
| locations[3].id | pmh:oai:doaj.org/article:bb1fbe848f6e42c4a65d7517f8d0a88a |
| 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 1-26 (2024) |
| locations[3].landing_page_url | https://doaj.org/article/bb1fbe848f6e42c4a65d7517f8d0a88a |
| locations[4].id | pmh:oai:pubmedcentral.nih.gov:11289125 |
| 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/11289125 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5106108873 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Hamid Anwar |
| authorships[0].countries | PK |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I90610274 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Civil Engineering, University of Engineering and Technology, Peshawar, 25000, Pakistan |
| authorships[0].institutions[0].id | https://openalex.org/I90610274 |
| authorships[0].institutions[0].ror | https://ror.org/00p034093 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I90610274 |
| authorships[0].institutions[0].country_code | PK |
| authorships[0].institutions[0].display_name | University of Engineering and Technology Peshawar |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Hamid Anwar |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Civil Engineering, University of Engineering and Technology, Peshawar, 25000, Pakistan |
| authorships[1].author.id | https://openalex.org/A5043905016 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-2742-0377 |
| authorships[1].author.display_name | Afed Ullah Khan |
| authorships[1].countries | PK |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I90610274 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Civil Engineering, University of Engineering and Technology, Peshawar, 25000, Pakistan |
| authorships[1].institutions[0].id | https://openalex.org/I90610274 |
| authorships[1].institutions[0].ror | https://ror.org/00p034093 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I90610274 |
| authorships[1].institutions[0].country_code | PK |
| authorships[1].institutions[0].display_name | University of Engineering and Technology Peshawar |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Afed Ullah Khan |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Civil Engineering, University of Engineering and Technology, Peshawar, 25000, Pakistan |
| authorships[2].author.id | https://openalex.org/A5112978662 |
| authorships[2].author.orcid | https://orcid.org/0009-0009-8285-5865 |
| authorships[2].author.display_name | Basir Ullah |
| authorships[2].countries | PK |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I90610274 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Civil Engineering, University of Engineering and Technology, Peshawar, 25000, Pakistan |
| authorships[2].institutions[0].id | https://openalex.org/I90610274 |
| authorships[2].institutions[0].ror | https://ror.org/00p034093 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I90610274 |
| authorships[2].institutions[0].country_code | PK |
| authorships[2].institutions[0].display_name | University of Engineering and Technology Peshawar |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Basir Ullah |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Civil Engineering, University of Engineering and Technology, Peshawar, 25000, Pakistan |
| authorships[3].author.id | https://openalex.org/A5108962868 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Abubakr Taha Bakheit Taha |
| authorships[3].countries | SA |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I142608572 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia |
| authorships[3].institutions[0].id | https://openalex.org/I142608572 |
| authorships[3].institutions[0].ror | https://ror.org/04jt46d36 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I142608572 |
| authorships[3].institutions[0].country_code | SA |
| authorships[3].institutions[0].display_name | Prince Sattam Bin Abdulaziz University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Abubakr Taha Bakheit Taha |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia |
| authorships[4].author.id | https://openalex.org/A5055599583 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-4895-5300 |
| authorships[4].author.display_name | Taoufik Najeh |
| authorships[4].countries | SE |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I190632392 |
| authorships[4].affiliations[0].raw_affiliation_string | Operation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Luleå, Sweden |
| authorships[4].institutions[0].id | https://openalex.org/I190632392 |
| authorships[4].institutions[0].ror | https://ror.org/016st3p78 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I190632392 |
| authorships[4].institutions[0].country_code | SE |
| authorships[4].institutions[0].display_name | Luleå University of Technology |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Taoufik Najeh |
| authorships[4].is_corresponding | True |
| authorships[4].raw_affiliation_strings | Operation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Luleå, Sweden |
| authorships[5].author.id | https://openalex.org/A5093804769 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Muhammad Usman Badshah |
| authorships[5].countries | PK |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I2799350497 |
| authorships[5].affiliations[0].raw_affiliation_string | Water and Power Division, Peshawar, Pakistan |
| authorships[5].institutions[0].id | https://openalex.org/I2799350497 |
| authorships[5].institutions[0].ror | https://ror.org/03revcz59 |
| authorships[5].institutions[0].type | government |
| authorships[5].institutions[0].lineage | https://openalex.org/I2799350497 |
| authorships[5].institutions[0].country_code | PK |
| authorships[5].institutions[0].display_name | Water and Power Development Authority |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Muhammad Usman Badshah |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Water and Power Division, Peshawar, Pakistan |
| authorships[6].author.id | https://openalex.org/A5075327791 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-4020-9402 |
| authorships[6].author.display_name | Abdulnoor A. J. Ghanim |
| authorships[6].countries | SA |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I47164929 |
| authorships[6].affiliations[0].raw_affiliation_string | Civil Engineering Department, College of Engineering, Najran University, 61441, Najran, Saudi Arabia |
| authorships[6].institutions[0].id | https://openalex.org/I47164929 |
| authorships[6].institutions[0].ror | https://ror.org/05edw4a90 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I47164929 |
| authorships[6].institutions[0].country_code | SA |
| authorships[6].institutions[0].display_name | Najran University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Abdulnoor A. J. Ghanim |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Civil Engineering Department, College of Engineering, Najran University, 61441, Najran, Saudi Arabia |
| authorships[7].author.id | https://openalex.org/A5101585405 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-9885-9547 |
| authorships[7].author.display_name | M. Irfan |
| authorships[7].countries | SA |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I47164929 |
| authorships[7].affiliations[0].raw_affiliation_string | Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, 61441, Najran, Saudi Arabia |
| authorships[7].institutions[0].id | https://openalex.org/I47164929 |
| authorships[7].institutions[0].ror | https://ror.org/05edw4a90 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I47164929 |
| authorships[7].institutions[0].country_code | SA |
| authorships[7].institutions[0].display_name | Najran University |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Muhammad Irfan |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, 61441, Najran, 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/s41598-024-63989-7.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6 |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10330 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9994999766349792 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2312 |
| primary_topic.subfield.display_name | Water Science and Technology |
| primary_topic.display_name | Hydrology and Watershed Management Studies |
| related_works | https://openalex.org/W4391375266, https://openalex.org/W2748952813, https://openalex.org/W2439644404, https://openalex.org/W3174343427, https://openalex.org/W2000979301, https://openalex.org/W98325756, https://openalex.org/W2061279527, https://openalex.org/W105199006, https://openalex.org/W4213385761, https://openalex.org/W2335811955 |
| cited_by_count | 12 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 11 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 5 |
| best_oa_location.id | doi:10.1038/s41598-024-63989-7 |
| 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-nc-nd |
| best_oa_location.pdf_url | https://www.nature.com/articles/s41598-024-63989-7.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| 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/s41598-024-63989-7 |
| primary_location.id | doi:10.1038/s41598-024-63989-7 |
| 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-nc-nd |
| primary_location.pdf_url | https://www.nature.com/articles/s41598-024-63989-7.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| 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/s41598-024-63989-7 |
| publication_date | 2024-07-29 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W3120976411, https://openalex.org/W4390225476, https://openalex.org/W4212891492, https://openalex.org/W3127543729, https://openalex.org/W3208967317, https://openalex.org/W4391032165, https://openalex.org/W2901255489, https://openalex.org/W3038268756, https://openalex.org/W3041500991, https://openalex.org/W3194796466, https://openalex.org/W4308488197, https://openalex.org/W4324257317, https://openalex.org/W4223926899, https://openalex.org/W4367186884, https://openalex.org/W4372351183, https://openalex.org/W4385754196, https://openalex.org/W2792047890, https://openalex.org/W1985479415, https://openalex.org/W3115653929, https://openalex.org/W3115369634, https://openalex.org/W3128290931, https://openalex.org/W3038342057, https://openalex.org/W2765402320, https://openalex.org/W6600182862, https://openalex.org/W4394578132, https://openalex.org/W4385729089, https://openalex.org/W2018028544, https://openalex.org/W2110047381, https://openalex.org/W2740253363, https://openalex.org/W4388825536, https://openalex.org/W4386642689, https://openalex.org/W4361210807, https://openalex.org/W4296918766, https://openalex.org/W4380998151, https://openalex.org/W2951992834 |
| referenced_works_count | 35 |
| abstract_inverted_index.DL | 113 |
| abstract_inverted_index.as | 124, 129, 145 |
| abstract_inverted_index.be | 227 |
| abstract_inverted_index.by | 109 |
| abstract_inverted_index.in | 238 |
| abstract_inverted_index.of | 67, 134, 161, 212 |
| abstract_inverted_index.on | 175, 210 |
| abstract_inverted_index.to | 5, 62, 170, 206 |
| abstract_inverted_index.585 | 49 |
| abstract_inverted_index.Lag | 33 |
| abstract_inverted_index.MME | 77, 101, 211 |
| abstract_inverted_index.The | 99, 112, 132, 164 |
| abstract_inverted_index.and | 38, 50, 56, 92, 105, 117, 122, 127, 159, 179, 189, 191, 199, 214, 232 |
| abstract_inverted_index.can | 226 |
| abstract_inverted_index.for | 9, 72, 85, 94, 102, 217, 229 |
| abstract_inverted_index.out | 4 |
| abstract_inverted_index.the | 10, 64, 81, 95, 171, 221, 239 |
| abstract_inverted_index.two | 44 |
| abstract_inverted_index.via | 80 |
| abstract_inverted_index.was | 2, 78, 107, 204 |
| abstract_inverted_index.(DL) | 19 |
| abstract_inverted_index.245. | 51 |
| abstract_inverted_index.Feed | 21 |
| abstract_inverted_index.Long | 39 |
| abstract_inverted_index.MAE, | 178 |
| abstract_inverted_index.MSE, | 177 |
| abstract_inverted_index.Swat | 11 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.Time | 32 |
| abstract_inverted_index.best | 65, 100 |
| abstract_inverted_index.deep | 17, 135 |
| abstract_inverted_index.four | 16 |
| abstract_inverted_index.make | 207 |
| abstract_inverted_index.mean | 147, 151, 155 |
| abstract_inverted_index.root | 146 |
| abstract_inverted_index.such | 144 |
| abstract_inverted_index.used | 61, 228 |
| abstract_inverted_index.were | 60, 115, 138 |
| abstract_inverted_index.year | 222 |
| abstract_inverted_index.(MME) | 75 |
| abstract_inverted_index.2100. | 223 |
| abstract_inverted_index.RMSE, | 176 |
| abstract_inverted_index.River | 12 |
| abstract_inverted_index.TLANN | 165, 203 |
| abstract_inverted_index.These | 224 |
| abstract_inverted_index.area. | 241 |
| abstract_inverted_index.based | 174, 209 |
| abstract_inverted_index.daily | 7 |
| abstract_inverted_index.error | 149, 153, 157 |
| abstract_inverted_index.other | 172 |
| abstract_inverted_index.study | 240 |
| abstract_inverted_index.three | 97 |
| abstract_inverted_index.under | 43 |
| abstract_inverted_index.until | 220 |
| abstract_inverted_index.using | 119, 140 |
| abstract_inverted_index.water | 235 |
| abstract_inverted_index.(65.25 | 183 |
| abstract_inverted_index.(72.06 | 193 |
| abstract_inverted_index.(GCMs) | 71 |
| abstract_inverted_index.(LSTM) | 42 |
| abstract_inverted_index.(MAE), | 158 |
| abstract_inverted_index.(MSE), | 154 |
| abstract_inverted_index.(SSPs) | 48 |
| abstract_inverted_index.46.793 | 187 |
| abstract_inverted_index.51.363 | 197 |
| abstract_inverted_index.Basin, | 13 |
| abstract_inverted_index.Forest | 83 |
| abstract_inverted_index.Memory | 41 |
| abstract_inverted_index.Models | 70 |
| abstract_inverted_index.Neural | 24, 29, 35 |
| abstract_inverted_index.Random | 54, 82 |
| abstract_inverted_index.SSP245 | 213 |
| abstract_inverted_index.SSP585 | 215 |
| abstract_inverted_index.Shared | 45 |
| abstract_inverted_index.Taylor | 52 |
| abstract_inverted_index.during | 181 |
| abstract_inverted_index.future | 218 |
| abstract_inverted_index.models | 114, 137, 173 |
| abstract_inverted_index.select | 63 |
| abstract_inverted_index.square | 148, 152 |
| abstract_inverted_index.tested | 118 |
| abstract_inverted_index.(RMSE), | 150 |
| abstract_inverted_index.(SANN), | 31 |
| abstract_inverted_index.(TLANN) | 37 |
| abstract_inverted_index.0.7443) | 200 |
| abstract_inverted_index.0.7978) | 190 |
| abstract_inverted_index.4256.97 | 185 |
| abstract_inverted_index.5192.95 | 195 |
| abstract_inverted_index.Forest, | 55 |
| abstract_inverted_index.Forward | 22 |
| abstract_inverted_index.General | 68 |
| abstract_inverted_index.Maximum | 86 |
| abstract_inverted_index.Minimum | 89 |
| abstract_inverted_index.carried | 3 |
| abstract_inverted_index.models: | 20 |
| abstract_inverted_index.predict | 6 |
| abstract_inverted_index.results | 133, 225 |
| abstract_inverted_index.testing | 192 |
| abstract_inverted_index.through | 15 |
| abstract_inverted_index.trained | 116 |
| abstract_inverted_index.(FFANN), | 26 |
| abstract_inverted_index.Boosting | 58 |
| abstract_inverted_index.Diagram, | 53 |
| abstract_inverted_index.Ensemble | 74 |
| abstract_inverted_index.Gradient | 57 |
| abstract_inverted_index.Networks | 25, 30, 36 |
| abstract_inverted_index.Pakistan | 14 |
| abstract_inverted_index.Pathways | 47 |
| abstract_inverted_index.Seasonal | 27 |
| abstract_inverted_index.absolute | 156 |
| abstract_inverted_index.compared | 169 |
| abstract_inverted_index.computed | 79 |
| abstract_inverted_index.learning | 18, 136 |
| abstract_inverted_index.observed | 120 |
| abstract_inverted_index.projects | 237 |
| abstract_inverted_index.rendered | 108 |
| abstract_inverted_index.research | 1 |
| abstract_inverted_index.superior | 167 |
| abstract_inverted_index.training | 182 |
| abstract_inverted_index.utilized | 205 |
| abstract_inverted_index.dependent | 130 |
| abstract_inverted_index.discharge | 128 |
| abstract_inverted_index.evaluated | 139 |
| abstract_inverted_index.planning, | 230 |
| abstract_inverted_index.regarding | 234 |
| abstract_inverted_index.resources | 236 |
| abstract_inverted_index.scenarios | 216 |
| abstract_inverted_index.technique | 84 |
| abstract_inverted_index.variables | 126 |
| abstract_inverted_index.Artificial | 23, 28, 34 |
| abstract_inverted_index.Compromise | 110 |
| abstract_inverted_index.Short-Term | 40 |
| abstract_inverted_index.indicators | 143 |
| abstract_inverted_index.streamflow | 8, 219 |
| abstract_inverted_index.techniques | 59 |
| abstract_inverted_index.variables. | 131 |
| abstract_inverted_index.Circulation | 69 |
| abstract_inverted_index.Multi-Model | 73 |
| abstract_inverted_index.Temperature | 87, 90 |
| abstract_inverted_index.coefficient | 160 |
| abstract_inverted_index.combination | 66 |
| abstract_inverted_index.independent | 125 |
| abstract_inverted_index.management, | 231 |
| abstract_inverted_index.performance | 142, 168 |
| abstract_inverted_index.predictions | 208 |
| abstract_inverted_index.statistical | 141 |
| abstract_inverted_index.techniques. | 98 |
| abstract_inverted_index.temperature | 123 |
| abstract_inverted_index.Programming. | 111 |
| abstract_inverted_index.computation. | 76 |
| abstract_inverted_index.demonstrated | 166 |
| abstract_inverted_index.R<sup>2</sup> | 180 |
| abstract_inverted_index.Socioeconomic | 46 |
| abstract_inverted_index.Subsequently, | 202 |
| abstract_inverted_index.determination | 162 |
| abstract_inverted_index.policy-making | 233 |
| abstract_inverted_index.precipitation | 93, 106, 121 |
| abstract_inverted_index.respectively. | 201 |
| abstract_inverted_index.aforementioned | 96 |
| abstract_inverted_index.m<sup>3</sup>/s | 188, 198 |
| abstract_inverted_index.(R<sup>2</sup>). | 163 |
| abstract_inverted_index.T<sub>max</sub>, | 103 |
| abstract_inverted_index.T<sub>min</sub>, | 104 |
| abstract_inverted_index.m<sup>3</sup>/s, | 184, 186, 194, 196 |
| abstract_inverted_index.(T<sub>max</sub>), | 88 |
| abstract_inverted_index.(T<sub>min</sub>), | 91 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5055599583 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I190632392 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.91502283 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |