A Method Based on Deep Learning for Severe Convective Weather Forecast: CNN-BiLSTM-AM (Version 1.0) Article Swipe
Jianbin Zhang
,
Meng Yin
,
Pu Wang
,
Zhiqiu Gao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/atmos15101229
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/atmos15101229
In this study, we propose a model called CNN-BiLSTM-AM that utilizes deep learning techniques to forecast severe convective weather based on ERA5 hourly data and observations. The model integrates a CNN with a Bidirectional Long Short-Term Memory (BiLSTM) system and an Attention Mechanism (AM). The CNN is tasked with extracting features from the input data, while the BiLSTM effectively captures temporal dependencies. The AM enhances the results by considering the impact of past feature states on severe weather phenomena. Additionally, we assess the performance of our model in comparison to traditional network architectures, including ConvLSTM, Predrnn++, CNN, FC-LSTM, and LSTM. Our results indicate that the CNN-BiLSTM-AM model exhibits superior accuracy in precipitation forecasting. Especially with the extension of the forecast time, the model performs well across multiple evaluation metrics. Furthermore, an interpretability analysis of the convective weather mechanisms utilizing machine learning highlights the critical role of total precipitable water (PWAT) in short-term heavy precipitation forecasts. It also emphasizes the significant impact of regional variables on convective weather patterns and the role of convective available potential energy (CAPE) in fostering conditions conducive to convection. These findings not only confirm the effectiveness of deep learning in the automatic identification of severe weather features but also validate the suitability of the sample dataset employed. Given its remarkable performance and robustness, we advocate for the adoption of this model to enhance the forecast of severe convective weather across various business applications.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/atmos15101229
- OA Status
- gold
- Cited By
- 5
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403406713
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4403406713Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/atmos15101229Digital Object Identifier
- Title
-
A Method Based on Deep Learning for Severe Convective Weather Forecast: CNN-BiLSTM-AM (Version 1.0)Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-15Full publication date if available
- Authors
-
Jianbin Zhang, Meng Yin, Pu Wang, Zhiqiu GaoList of authors in order
- Landing page
-
https://doi.org/10.3390/atmos15101229Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/atmos15101229Direct OA link when available
- Concepts
-
Meteorology, Environmental science, Severe weather, Climatology, Convection, Artificial intelligence, Computer science, Geology, Geography, StormTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4403406713 |
|---|---|
| doi | https://doi.org/10.3390/atmos15101229 |
| ids.doi | https://doi.org/10.3390/atmos15101229 |
| ids.openalex | https://openalex.org/W4403406713 |
| fwci | 2.81312708 |
| type | article |
| title | A Method Based on Deep Learning for Severe Convective Weather Forecast: CNN-BiLSTM-AM (Version 1.0) |
| awards[0].id | https://openalex.org/G538002743 |
| awards[0].funder_id | https://openalex.org/F4320321001 |
| awards[0].display_name | |
| awards[0].funder_award_id | 41875013 |
| awards[0].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | 10 |
| biblio.volume | 15 |
| biblio.last_page | 1229 |
| biblio.first_page | 1229 |
| topics[0].id | https://openalex.org/T10466 |
| topics[0].field.id | https://openalex.org/fields/19 |
| topics[0].field.display_name | Earth and Planetary Sciences |
| topics[0].score | 0.9990000128746033 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1902 |
| topics[0].subfield.display_name | Atmospheric Science |
| topics[0].display_name | Meteorological Phenomena and Simulations |
| topics[1].id | https://openalex.org/T13018 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9950000047683716 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Seismology and Earthquake Studies |
| 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 | 0.9922999739646912 |
| 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 |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| is_xpac | False |
| apc_list.value | 2000 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2165 |
| apc_paid.value | 2000 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2165 |
| concepts[0].id | https://openalex.org/C153294291 |
| concepts[0].level | 1 |
| concepts[0].score | 0.6226087808609009 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q25261 |
| concepts[0].display_name | Meteorology |
| concepts[1].id | https://openalex.org/C39432304 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5368457436561584 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[1].display_name | Environmental science |
| concepts[2].id | https://openalex.org/C187460315 |
| concepts[2].level | 3 |
| concepts[2].score | 0.4750021696090698 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q741507 |
| concepts[2].display_name | Severe weather |
| concepts[3].id | https://openalex.org/C49204034 |
| concepts[3].level | 1 |
| concepts[3].score | 0.4492918848991394 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q52139 |
| concepts[3].display_name | Climatology |
| concepts[4].id | https://openalex.org/C10899652 |
| concepts[4].level | 2 |
| concepts[4].score | 0.447689950466156 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q160329 |
| concepts[4].display_name | Convection |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.38243600726127625 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C41008148 |
| concepts[6].level | 0 |
| concepts[6].score | 0.363250195980072 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[6].display_name | Computer science |
| concepts[7].id | https://openalex.org/C127313418 |
| concepts[7].level | 0 |
| concepts[7].score | 0.2164052426815033 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[7].display_name | Geology |
| concepts[8].id | https://openalex.org/C205649164 |
| concepts[8].level | 0 |
| concepts[8].score | 0.16288435459136963 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[8].display_name | Geography |
| concepts[9].id | https://openalex.org/C105306849 |
| concepts[9].level | 2 |
| concepts[9].score | 0.08758807182312012 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q81054 |
| concepts[9].display_name | Storm |
| keywords[0].id | https://openalex.org/keywords/meteorology |
| keywords[0].score | 0.6226087808609009 |
| keywords[0].display_name | Meteorology |
| keywords[1].id | https://openalex.org/keywords/environmental-science |
| keywords[1].score | 0.5368457436561584 |
| keywords[1].display_name | Environmental science |
| keywords[2].id | https://openalex.org/keywords/severe-weather |
| keywords[2].score | 0.4750021696090698 |
| keywords[2].display_name | Severe weather |
| keywords[3].id | https://openalex.org/keywords/climatology |
| keywords[3].score | 0.4492918848991394 |
| keywords[3].display_name | Climatology |
| keywords[4].id | https://openalex.org/keywords/convection |
| keywords[4].score | 0.447689950466156 |
| keywords[4].display_name | Convection |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.38243600726127625 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/computer-science |
| keywords[6].score | 0.363250195980072 |
| keywords[6].display_name | Computer science |
| keywords[7].id | https://openalex.org/keywords/geology |
| keywords[7].score | 0.2164052426815033 |
| keywords[7].display_name | Geology |
| keywords[8].id | https://openalex.org/keywords/geography |
| keywords[8].score | 0.16288435459136963 |
| keywords[8].display_name | Geography |
| keywords[9].id | https://openalex.org/keywords/storm |
| keywords[9].score | 0.08758807182312012 |
| keywords[9].display_name | Storm |
| language | en |
| locations[0].id | doi:10.3390/atmos15101229 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S946213959 |
| locations[0].source.issn | 2073-4433 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2073-4433 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Atmosphere |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| 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 | Atmosphere |
| locations[0].landing_page_url | https://doi.org/10.3390/atmos15101229 |
| locations[1].id | pmh:oai:doaj.org/article:70a166a6424e4e13bc43cc67a1dd70c7 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| 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 | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Atmosphere, Vol 15, Iss 10, p 1229 (2024) |
| locations[1].landing_page_url | https://doaj.org/article/70a166a6424e4e13bc43cc67a1dd70c7 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5100616593 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Jianbin Zhang |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I200845125 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China |
| authorships[0].institutions[0].id | https://openalex.org/I200845125 |
| authorships[0].institutions[0].ror | https://ror.org/02y0rxk19 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I200845125 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Nanjing University of Information Science and Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jianbin Zhang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China |
| authorships[1].author.id | https://openalex.org/A5012159722 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-7173-3269 |
| authorships[1].author.display_name | Meng Yin |
| authorships[1].affiliations[0].raw_affiliation_string | Kaifeng Power Supply Company of Henan Electric Power Company of State Grid, Kaifeng 475000, China |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Meng Yin |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Kaifeng Power Supply Company of Henan Electric Power Company of State Grid, Kaifeng 475000, China |
| authorships[2].author.id | https://openalex.org/A5100383518 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-4718-3102 |
| authorships[2].author.display_name | Pu Wang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I154099455 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Environmental Science and Engineering, Shandong University, Qingdao 266237, China |
| authorships[2].institutions[0].id | https://openalex.org/I154099455 |
| authorships[2].institutions[0].ror | https://ror.org/0207yh398 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I154099455 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Shandong University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Pu Wang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Environmental Science and Engineering, Shandong University, Qingdao 266237, China |
| authorships[3].author.id | https://openalex.org/A5077538403 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-8256-005X |
| authorships[3].author.display_name | Zhiqiu Gao |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I200845125 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China |
| authorships[3].institutions[0].id | https://openalex.org/I200845125 |
| authorships[3].institutions[0].ror | https://ror.org/02y0rxk19 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I200845125 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Nanjing University of Information Science and Technology |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Zhiqiu Gao |
| authorships[3].is_corresponding | True |
| authorships[3].raw_affiliation_strings | School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.3390/atmos15101229 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Method Based on Deep Learning for Severe Convective Weather Forecast: CNN-BiLSTM-AM (Version 1.0) |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10466 |
| primary_topic.field.id | https://openalex.org/fields/19 |
| primary_topic.field.display_name | Earth and Planetary Sciences |
| primary_topic.score | 0.9990000128746033 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1902 |
| primary_topic.subfield.display_name | Atmospheric Science |
| primary_topic.display_name | Meteorological Phenomena and Simulations |
| related_works | https://openalex.org/W2142460879, https://openalex.org/W2070226858, https://openalex.org/W2038536450, https://openalex.org/W2795584399, https://openalex.org/W2137586905, https://openalex.org/W2415593630, https://openalex.org/W752606210, https://openalex.org/W3191237879, https://openalex.org/W2139798740, https://openalex.org/W4252200661 |
| cited_by_count | 5 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 4 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3390/atmos15101229 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S946213959 |
| best_oa_location.source.issn | 2073-4433 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2073-4433 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Atmosphere |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| 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 | Atmosphere |
| best_oa_location.landing_page_url | https://doi.org/10.3390/atmos15101229 |
| primary_location.id | doi:10.3390/atmos15101229 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S946213959 |
| primary_location.source.issn | 2073-4433 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2073-4433 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Atmosphere |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| 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 | Atmosphere |
| primary_location.landing_page_url | https://doi.org/10.3390/atmos15101229 |
| publication_date | 2024-10-15 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2121734470, https://openalex.org/W2394097049, https://openalex.org/W2749457555, https://openalex.org/W4294687141, https://openalex.org/W4223559747, https://openalex.org/W3180126160, https://openalex.org/W4289531761, https://openalex.org/W2607123227, https://openalex.org/W1969712052, https://openalex.org/W2178452323, https://openalex.org/W2020977646, https://openalex.org/W1986133615, https://openalex.org/W2042570774, https://openalex.org/W2023308405, https://openalex.org/W1979896728, https://openalex.org/W2048046425, https://openalex.org/W1831098712, https://openalex.org/W2165222966, https://openalex.org/W2601923741, https://openalex.org/W2913323966, https://openalex.org/W2801493395, https://openalex.org/W2982788905, https://openalex.org/W2163605009, https://openalex.org/W1538131130, https://openalex.org/W6679349572, https://openalex.org/W2983574496, https://openalex.org/W2500487046, https://openalex.org/W4383218913, https://openalex.org/W4388654737, https://openalex.org/W3025949386, https://openalex.org/W2123987184, https://openalex.org/W2085144721, https://openalex.org/W1717198923, https://openalex.org/W875569023, https://openalex.org/W1750477509, https://openalex.org/W2605448180, https://openalex.org/W2577888971, https://openalex.org/W2944123510, https://openalex.org/W2338318698, https://openalex.org/W2767106145, https://openalex.org/W3110378470, https://openalex.org/W6744750712, https://openalex.org/W6628877408, https://openalex.org/W3138340468, https://openalex.org/W2985739459, https://openalex.org/W2767264170, https://openalex.org/W2898796705, https://openalex.org/W2969309273, https://openalex.org/W937396684, https://openalex.org/W6959164297, https://openalex.org/W1485009520, https://openalex.org/W2762410434 |
| referenced_works_count | 52 |
| abstract_inverted_index.a | 5, 29, 32 |
| abstract_inverted_index.AM | 63 |
| abstract_inverted_index.In | 0 |
| abstract_inverted_index.It | 155 |
| abstract_inverted_index.an | 40, 130 |
| abstract_inverted_index.by | 67 |
| abstract_inverted_index.in | 87, 110, 150, 177, 193 |
| abstract_inverted_index.is | 46 |
| abstract_inverted_index.of | 71, 84, 117, 133, 145, 161, 171, 190, 197, 206, 222, 229 |
| abstract_inverted_index.on | 20, 75, 164 |
| abstract_inverted_index.to | 14, 89, 181, 225 |
| abstract_inverted_index.we | 3, 80, 217 |
| abstract_inverted_index.CNN | 30, 45 |
| abstract_inverted_index.Our | 100 |
| abstract_inverted_index.The | 26, 44, 62 |
| abstract_inverted_index.and | 24, 39, 98, 168, 215 |
| abstract_inverted_index.but | 201 |
| abstract_inverted_index.for | 219 |
| abstract_inverted_index.its | 212 |
| abstract_inverted_index.not | 185 |
| abstract_inverted_index.our | 85 |
| abstract_inverted_index.the | 52, 56, 65, 69, 82, 104, 115, 118, 121, 134, 142, 158, 169, 188, 194, 204, 207, 220, 227 |
| abstract_inverted_index.CNN, | 96 |
| abstract_inverted_index.ERA5 | 21 |
| abstract_inverted_index.Long | 34 |
| abstract_inverted_index.also | 156, 202 |
| abstract_inverted_index.data | 23 |
| abstract_inverted_index.deep | 11, 191 |
| abstract_inverted_index.from | 51 |
| abstract_inverted_index.only | 186 |
| abstract_inverted_index.past | 72 |
| abstract_inverted_index.role | 144, 170 |
| abstract_inverted_index.that | 9, 103 |
| abstract_inverted_index.this | 1, 223 |
| abstract_inverted_index.well | 124 |
| abstract_inverted_index.with | 31, 48, 114 |
| abstract_inverted_index.(AM). | 43 |
| abstract_inverted_index.Given | 211 |
| abstract_inverted_index.LSTM. | 99 |
| abstract_inverted_index.These | 183 |
| abstract_inverted_index.based | 19 |
| abstract_inverted_index.data, | 54 |
| abstract_inverted_index.heavy | 152 |
| abstract_inverted_index.input | 53 |
| abstract_inverted_index.model | 6, 27, 86, 106, 122, 224 |
| abstract_inverted_index.time, | 120 |
| abstract_inverted_index.total | 146 |
| abstract_inverted_index.water | 148 |
| abstract_inverted_index.while | 55 |
| abstract_inverted_index.(CAPE) | 176 |
| abstract_inverted_index.(PWAT) | 149 |
| abstract_inverted_index.BiLSTM | 57 |
| abstract_inverted_index.Memory | 36 |
| abstract_inverted_index.across | 125, 233 |
| abstract_inverted_index.assess | 81 |
| abstract_inverted_index.called | 7 |
| abstract_inverted_index.energy | 175 |
| abstract_inverted_index.hourly | 22 |
| abstract_inverted_index.impact | 70, 160 |
| abstract_inverted_index.sample | 208 |
| abstract_inverted_index.severe | 16, 76, 198, 230 |
| abstract_inverted_index.states | 74 |
| abstract_inverted_index.study, | 2 |
| abstract_inverted_index.system | 38 |
| abstract_inverted_index.tasked | 47 |
| abstract_inverted_index.confirm | 187 |
| abstract_inverted_index.dataset | 209 |
| abstract_inverted_index.enhance | 226 |
| abstract_inverted_index.feature | 73 |
| abstract_inverted_index.machine | 139 |
| abstract_inverted_index.network | 91 |
| abstract_inverted_index.propose | 4 |
| abstract_inverted_index.results | 66, 101 |
| abstract_inverted_index.various | 234 |
| abstract_inverted_index.weather | 18, 77, 136, 166, 199, 232 |
| abstract_inverted_index.(BiLSTM) | 37 |
| abstract_inverted_index.FC-LSTM, | 97 |
| abstract_inverted_index.accuracy | 109 |
| abstract_inverted_index.adoption | 221 |
| abstract_inverted_index.advocate | 218 |
| abstract_inverted_index.analysis | 132 |
| abstract_inverted_index.business | 235 |
| abstract_inverted_index.captures | 59 |
| abstract_inverted_index.critical | 143 |
| abstract_inverted_index.enhances | 64 |
| abstract_inverted_index.exhibits | 107 |
| abstract_inverted_index.features | 50, 200 |
| abstract_inverted_index.findings | 184 |
| abstract_inverted_index.forecast | 15, 119, 228 |
| abstract_inverted_index.indicate | 102 |
| abstract_inverted_index.learning | 12, 140, 192 |
| abstract_inverted_index.metrics. | 128 |
| abstract_inverted_index.multiple | 126 |
| abstract_inverted_index.patterns | 167 |
| abstract_inverted_index.performs | 123 |
| abstract_inverted_index.regional | 162 |
| abstract_inverted_index.superior | 108 |
| abstract_inverted_index.temporal | 60 |
| abstract_inverted_index.utilizes | 10 |
| abstract_inverted_index.validate | 203 |
| abstract_inverted_index.Attention | 41 |
| abstract_inverted_index.ConvLSTM, | 94 |
| abstract_inverted_index.Mechanism | 42 |
| abstract_inverted_index.automatic | 195 |
| abstract_inverted_index.available | 173 |
| abstract_inverted_index.conducive | 180 |
| abstract_inverted_index.employed. | 210 |
| abstract_inverted_index.extension | 116 |
| abstract_inverted_index.fostering | 178 |
| abstract_inverted_index.including | 93 |
| abstract_inverted_index.potential | 174 |
| abstract_inverted_index.utilizing | 138 |
| abstract_inverted_index.variables | 163 |
| abstract_inverted_index.Especially | 113 |
| abstract_inverted_index.Predrnn++, | 95 |
| abstract_inverted_index.Short-Term | 35 |
| abstract_inverted_index.comparison | 88 |
| abstract_inverted_index.conditions | 179 |
| abstract_inverted_index.convective | 17, 135, 165, 172, 231 |
| abstract_inverted_index.emphasizes | 157 |
| abstract_inverted_index.evaluation | 127 |
| abstract_inverted_index.extracting | 49 |
| abstract_inverted_index.forecasts. | 154 |
| abstract_inverted_index.highlights | 141 |
| abstract_inverted_index.integrates | 28 |
| abstract_inverted_index.mechanisms | 137 |
| abstract_inverted_index.phenomena. | 78 |
| abstract_inverted_index.remarkable | 213 |
| abstract_inverted_index.short-term | 151 |
| abstract_inverted_index.techniques | 13 |
| abstract_inverted_index.considering | 68 |
| abstract_inverted_index.convection. | 182 |
| abstract_inverted_index.effectively | 58 |
| abstract_inverted_index.performance | 83, 214 |
| abstract_inverted_index.robustness, | 216 |
| abstract_inverted_index.significant | 159 |
| abstract_inverted_index.suitability | 205 |
| abstract_inverted_index.traditional | 90 |
| abstract_inverted_index.Furthermore, | 129 |
| abstract_inverted_index.forecasting. | 112 |
| abstract_inverted_index.precipitable | 147 |
| abstract_inverted_index.Additionally, | 79 |
| abstract_inverted_index.Bidirectional | 33 |
| abstract_inverted_index.CNN-BiLSTM-AM | 8, 105 |
| abstract_inverted_index.applications. | 236 |
| abstract_inverted_index.dependencies. | 61 |
| abstract_inverted_index.effectiveness | 189 |
| abstract_inverted_index.observations. | 25 |
| abstract_inverted_index.precipitation | 111, 153 |
| abstract_inverted_index.architectures, | 92 |
| abstract_inverted_index.identification | 196 |
| abstract_inverted_index.interpretability | 131 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5077538403 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I200845125 |
| citation_normalized_percentile.value | 0.8928874 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |