Risk Assessment and Its Visualization of Power Tower under Typhoon Disaster Based on Machine Learning Algorithms Article Swipe
Hui Hou
,
Shiwen Yu
,
Hongbin Wang
,
Yong Huang
,
Hao Wu
,
Yan Xu
,
Xianqiang Li
,
Hao Geng
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/en12020205
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/en12020205
For power system disaster prevention and mitigation, risk assessment and visualization under typhoon disaster have important scientific significance and engineering value. However, current studies have problems such as incomplete factors, strong subjectivity, complicated calculations, and so on. Therefore, a novel risk assessment and its visualization system consisting of a data layer, knowledge extraction layer, and visualization layer on power towers under typhoon disaster are proposed. On the data layer, a spatial multi-source heterogeneous information database is built based on equipment operation information, meteorological information, and geographic information. On the knowledge extraction layer, six intelligent risk prediction models are established based on machine learning algorithms by hyperparameter optimization. Then the relative optimal model is selected by comparing five evaluation indicators, and the combined model consisting of five relatively superior models is established by goodness of fit method with unequal weight. On the visualization layer, the predicted results are visualized with accuracy of 1 km × 1 km by ArcGIS 10.4. In results, the power tower damage risk assessment is carried out in a Chinese coastal city under the typhoon ‘Mujigae’. By comparing predicted distribution and similarity indicator of the combined model with those of the other models, it is shown that the combined model is superior not only in quality but also in quantity.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en12020205
- https://www.mdpi.com/1996-1073/12/2/205/pdf?version=1547107752
- OA Status
- gold
- Cited By
- 37
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2908940358
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2908940358Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/en12020205Digital Object Identifier
- Title
-
Risk Assessment and Its Visualization of Power Tower under Typhoon Disaster Based on Machine Learning AlgorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-09Full publication date if available
- Authors
-
Hui Hou, Shiwen Yu, Hongbin Wang, Yong Huang, Hao Wu, Yan Xu, Xianqiang Li, Hao GengList of authors in order
- Landing page
-
https://doi.org/10.3390/en12020205Publisher landing page
- PDF URL
-
https://www.mdpi.com/1996-1073/12/2/205/pdf?version=1547107752Direct 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.mdpi.com/1996-1073/12/2/205/pdf?version=1547107752Direct OA link when available
- Concepts
-
Typhoon, Visualization, Computer science, Data mining, Risk assessment, Artificial intelligence, Machine learning, Reliability engineering, Algorithm, Engineering, Meteorology, Geography, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
37Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 12, 2024: 6, 2023: 5, 2022: 3, 2021: 7Per-year citation counts (last 5 years)
- References (count)
-
20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2908940358 |
|---|---|
| doi | https://doi.org/10.3390/en12020205 |
| ids.doi | https://doi.org/10.3390/en12020205 |
| ids.mag | 2908940358 |
| ids.openalex | https://openalex.org/W2908940358 |
| fwci | 1.59565061 |
| type | article |
| title | Risk Assessment and Its Visualization of Power Tower under Typhoon Disaster Based on Machine Learning Algorithms |
| biblio.issue | 2 |
| biblio.volume | 12 |
| biblio.last_page | 205 |
| biblio.first_page | 205 |
| topics[0].id | https://openalex.org/T11483 |
| topics[0].field.id | https://openalex.org/fields/19 |
| topics[0].field.display_name | Earth and Planetary Sciences |
| topics[0].score | 0.996999979019165 |
| 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 | Tropical and Extratropical Cyclones Research |
| topics[1].id | https://openalex.org/T10930 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.996399998664856 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2306 |
| topics[1].subfield.display_name | Global and Planetary Change |
| topics[1].display_name | Flood Risk Assessment and Management |
| topics[2].id | https://openalex.org/T11807 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9919000267982483 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2205 |
| topics[2].subfield.display_name | Civil and Structural Engineering |
| topics[2].display_name | Infrastructure Resilience and Vulnerability Analysis |
| is_xpac | False |
| apc_list.value | 2200 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2382 |
| apc_paid.value | 2200 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2382 |
| concepts[0].id | https://openalex.org/C181654704 |
| concepts[0].level | 2 |
| concepts[0].score | 0.728675901889801 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q140588 |
| concepts[0].display_name | Typhoon |
| concepts[1].id | https://openalex.org/C36464697 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7163218855857849 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q451553 |
| concepts[1].display_name | Visualization |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6596239805221558 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C124101348 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5301571488380432 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[3].display_name | Data mining |
| concepts[4].id | https://openalex.org/C12174686 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4145236015319824 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1058438 |
| concepts[4].display_name | Risk assessment |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.36415374279022217 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C119857082 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3548853397369385 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[6].display_name | Machine learning |
| concepts[7].id | https://openalex.org/C200601418 |
| concepts[7].level | 1 |
| concepts[7].score | 0.34111273288726807 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2193887 |
| concepts[7].display_name | Reliability engineering |
| concepts[8].id | https://openalex.org/C11413529 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3372233510017395 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[8].display_name | Algorithm |
| concepts[9].id | https://openalex.org/C127413603 |
| concepts[9].level | 0 |
| concepts[9].score | 0.17300722002983093 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[9].display_name | Engineering |
| concepts[10].id | https://openalex.org/C153294291 |
| concepts[10].level | 1 |
| concepts[10].score | 0.14952382445335388 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q25261 |
| concepts[10].display_name | Meteorology |
| concepts[11].id | https://openalex.org/C205649164 |
| concepts[11].level | 0 |
| concepts[11].score | 0.10021588206291199 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[11].display_name | Geography |
| concepts[12].id | https://openalex.org/C38652104 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[12].display_name | Computer security |
| keywords[0].id | https://openalex.org/keywords/typhoon |
| keywords[0].score | 0.728675901889801 |
| keywords[0].display_name | Typhoon |
| keywords[1].id | https://openalex.org/keywords/visualization |
| keywords[1].score | 0.7163218855857849 |
| keywords[1].display_name | Visualization |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6596239805221558 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/data-mining |
| keywords[3].score | 0.5301571488380432 |
| keywords[3].display_name | Data mining |
| keywords[4].id | https://openalex.org/keywords/risk-assessment |
| keywords[4].score | 0.4145236015319824 |
| keywords[4].display_name | Risk assessment |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.36415374279022217 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/machine-learning |
| keywords[6].score | 0.3548853397369385 |
| keywords[6].display_name | Machine learning |
| keywords[7].id | https://openalex.org/keywords/reliability-engineering |
| keywords[7].score | 0.34111273288726807 |
| keywords[7].display_name | Reliability engineering |
| keywords[8].id | https://openalex.org/keywords/algorithm |
| keywords[8].score | 0.3372233510017395 |
| keywords[8].display_name | Algorithm |
| keywords[9].id | https://openalex.org/keywords/engineering |
| keywords[9].score | 0.17300722002983093 |
| keywords[9].display_name | Engineering |
| keywords[10].id | https://openalex.org/keywords/meteorology |
| keywords[10].score | 0.14952382445335388 |
| keywords[10].display_name | Meteorology |
| keywords[11].id | https://openalex.org/keywords/geography |
| keywords[11].score | 0.10021588206291199 |
| keywords[11].display_name | Geography |
| language | en |
| locations[0].id | doi:10.3390/en12020205 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S198098182 |
| locations[0].source.issn | 1996-1073 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1996-1073 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Energies |
| 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 | https://www.mdpi.com/1996-1073/12/2/205/pdf?version=1547107752 |
| 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 | Energies |
| locations[0].landing_page_url | https://doi.org/10.3390/en12020205 |
| locations[1].id | pmh:oai:RePEc:gam:jeners:v:12:y:2019:i:2:p:205-:d:196272 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401271 |
| 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 | RePEc: Research Papers in Economics |
| locations[1].source.host_organization | https://openalex.org/I77793887 |
| locations[1].source.host_organization_name | Federal Reserve Bank of St. Louis |
| locations[1].source.host_organization_lineage | https://openalex.org/I77793887 |
| 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 | |
| locations[1].landing_page_url | https://www.mdpi.com/1996-1073/12/2/205/ |
| locations[2].id | pmh:oai:doaj.org/article:2364e3ac18a147e3a89bdb1da04dc37d |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306401280 |
| 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 | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | cc-by-sa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Energies, Vol 12, Iss 2, p 205 (2019) |
| locations[2].landing_page_url | https://doaj.org/article/2364e3ac18a147e3a89bdb1da04dc37d |
| locations[3].id | pmh:oai:dr.ntu.edu.sg:10356/106260 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306402609 |
| 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 | DR-NTU (Nanyang Technological University) |
| locations[3].source.host_organization | https://openalex.org/I172675005 |
| locations[3].source.host_organization_name | Nanyang Technological University |
| locations[3].source.host_organization_lineage | https://openalex.org/I172675005 |
| locations[3].license | cc-by |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Journal Article |
| locations[3].license_id | https://openalex.org/licenses/cc-by |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | |
| locations[3].landing_page_url | https://hdl.handle.net/10356/106260 |
| locations[4].id | pmh:oai:mdpi.com:/1996-1073/12/2/205/ |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S4306400947 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | True |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | MDPI (MDPI AG) |
| locations[4].source.host_organization | https://openalex.org/I4210097602 |
| locations[4].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[4].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[4].license | cc-by |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | Text |
| locations[4].license_id | https://openalex.org/licenses/cc-by |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Energies; Volume 12; Issue 2; Pages: 205 |
| locations[4].landing_page_url | https://dx.doi.org/10.3390/en12020205 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5005736265 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1384-5515 |
| authorships[0].author.display_name | Hui Hou |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I196699116 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Automation, Wuhan University of Technology, Wuhan 430070, China |
| authorships[0].institutions[0].id | https://openalex.org/I196699116 |
| authorships[0].institutions[0].ror | https://ror.org/03fe7t173 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I196699116 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Wuhan University of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Hui Hou |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Automation, Wuhan University of Technology, Wuhan 430070, China |
| authorships[1].author.id | https://openalex.org/A5052324373 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-8514-3195 |
| authorships[1].author.display_name | Shiwen Yu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I196699116 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Automation, Wuhan University of Technology, Wuhan 430070, China |
| authorships[1].institutions[0].id | https://openalex.org/I196699116 |
| authorships[1].institutions[0].ror | https://ror.org/03fe7t173 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I196699116 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Wuhan University of Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Shiwen Yu |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | School of Automation, Wuhan University of Technology, Wuhan 430070, China |
| authorships[2].author.id | https://openalex.org/A5100332076 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-5273-3618 |
| authorships[2].author.display_name | Hongbin Wang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210086825 |
| authorships[2].affiliations[0].raw_affiliation_string | Guangzhou Power Supply Bureau Co., Ltd., Guangzhou 510620, China |
| authorships[2].institutions[0].id | https://openalex.org/I4210086825 |
| authorships[2].institutions[0].ror | https://ror.org/00479t136 |
| authorships[2].institutions[0].type | government |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210086825 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Guangzhou Education Bureau |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Hongbin Wang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Guangzhou Power Supply Bureau Co., Ltd., Guangzhou 510620, China |
| authorships[3].author.id | https://openalex.org/A5006384169 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-7963-0720 |
| authorships[3].author.display_name | Yong Huang |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I74872605 |
| authorships[3].affiliations[0].raw_affiliation_string | GuangDong Power GRID Co., Ltd., Electric Power Research Institute, Guangzhou 510080, China |
| authorships[3].institutions[0].id | https://openalex.org/I74872605 |
| authorships[3].institutions[0].ror | https://ror.org/03hkh9419 |
| authorships[3].institutions[0].type | company |
| authorships[3].institutions[0].lineage | https://openalex.org/I74872605 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | China Southern Power Grid (China) |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Yong Huang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | GuangDong Power GRID Co., Ltd., Electric Power Research Institute, Guangzhou 510080, China |
| authorships[4].author.id | https://openalex.org/A5074969115 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-6471-1774 |
| authorships[4].author.display_name | Hao Wu |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I74872605 |
| authorships[4].affiliations[0].raw_affiliation_string | GuangDong Power GRID Co., Ltd., Electric Power Research Institute, Guangzhou 510080, China |
| authorships[4].institutions[0].id | https://openalex.org/I74872605 |
| authorships[4].institutions[0].ror | https://ror.org/03hkh9419 |
| authorships[4].institutions[0].type | company |
| authorships[4].institutions[0].lineage | https://openalex.org/I74872605 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | China Southern Power Grid (China) |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Hao Wu |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | GuangDong Power GRID Co., Ltd., Electric Power Research Institute, Guangzhou 510080, China |
| authorships[5].author.id | https://openalex.org/A5013947981 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-0503-183X |
| authorships[5].author.display_name | Yan Xu |
| authorships[5].countries | SG |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I172675005 |
| authorships[5].affiliations[0].raw_affiliation_string | Department, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore |
| authorships[5].institutions[0].id | https://openalex.org/I172675005 |
| authorships[5].institutions[0].ror | https://ror.org/02e7b5302 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I172675005 |
| authorships[5].institutions[0].country_code | SG |
| authorships[5].institutions[0].display_name | Nanyang Technological University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Yan Xu |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore |
| authorships[6].author.id | https://openalex.org/A5021918980 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-7420-6253 |
| authorships[6].author.display_name | Xianqiang Li |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I196699116 |
| authorships[6].affiliations[0].raw_affiliation_string | School of Automation, Wuhan University of Technology, Wuhan 430070, China |
| authorships[6].institutions[0].id | https://openalex.org/I196699116 |
| authorships[6].institutions[0].ror | https://ror.org/03fe7t173 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I196699116 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Wuhan University of Technology |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Xianqiang Li |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | School of Automation, Wuhan University of Technology, Wuhan 430070, China |
| authorships[7].author.id | https://openalex.org/A5101413344 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-0943-7714 |
| authorships[7].author.display_name | Hao Geng |
| authorships[7].countries | CN |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I196699116 |
| authorships[7].affiliations[0].raw_affiliation_string | School of Automation, Wuhan University of Technology, Wuhan 430070, China |
| authorships[7].institutions[0].id | https://openalex.org/I196699116 |
| authorships[7].institutions[0].ror | https://ror.org/03fe7t173 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I196699116 |
| authorships[7].institutions[0].country_code | CN |
| authorships[7].institutions[0].display_name | Wuhan University of Technology |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Hao Geng |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | School of Automation, Wuhan University of Technology, Wuhan 430070, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1996-1073/12/2/205/pdf?version=1547107752 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Risk Assessment and Its Visualization of Power Tower under Typhoon Disaster Based on Machine Learning Algorithms |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11483 |
| primary_topic.field.id | https://openalex.org/fields/19 |
| primary_topic.field.display_name | Earth and Planetary Sciences |
| primary_topic.score | 0.996999979019165 |
| 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 | Tropical and Extratropical Cyclones Research |
| related_works | https://openalex.org/W2322294131, https://openalex.org/W4377023816, https://openalex.org/W2044728225, https://openalex.org/W2381913349, https://openalex.org/W3125308484, https://openalex.org/W2368202014, https://openalex.org/W2001663785, https://openalex.org/W1526491517, https://openalex.org/W2787131064, https://openalex.org/W2903862324 |
| cited_by_count | 37 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 12 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 6 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 5 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 3 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 7 |
| counts_by_year[5].year | 2020 |
| counts_by_year[5].cited_by_count | 3 |
| counts_by_year[6].year | 2019 |
| counts_by_year[6].cited_by_count | 1 |
| locations_count | 5 |
| best_oa_location.id | doi:10.3390/en12020205 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S198098182 |
| best_oa_location.source.issn | 1996-1073 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1996-1073 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Energies |
| 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 | https://www.mdpi.com/1996-1073/12/2/205/pdf?version=1547107752 |
| 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 | Energies |
| best_oa_location.landing_page_url | https://doi.org/10.3390/en12020205 |
| primary_location.id | doi:10.3390/en12020205 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S198098182 |
| primary_location.source.issn | 1996-1073 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1996-1073 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Energies |
| 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 | https://www.mdpi.com/1996-1073/12/2/205/pdf?version=1547107752 |
| 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 | Energies |
| primary_location.landing_page_url | https://doi.org/10.3390/en12020205 |
| publication_date | 2019-01-09 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W3003874575, https://openalex.org/W2021831421, https://openalex.org/W2515275729, https://openalex.org/W2510577477, https://openalex.org/W2791884162, https://openalex.org/W2064462974, https://openalex.org/W2014910243, https://openalex.org/W1832102335, https://openalex.org/W2011566106, https://openalex.org/W2009267533, https://openalex.org/W6705997868, https://openalex.org/W2267090150, https://openalex.org/W1988790447, https://openalex.org/W6676769703, https://openalex.org/W1678356000, https://openalex.org/W2911964244, https://openalex.org/W6996555208, https://openalex.org/W2354854668, https://openalex.org/W2388884622, https://openalex.org/W2347729430 |
| referenced_works_count | 20 |
| abstract_inverted_index.1 | 151, 154 |
| abstract_inverted_index.a | 38, 48, 69, 171 |
| abstract_inverted_index.By | 179 |
| abstract_inverted_index.In | 159 |
| abstract_inverted_index.On | 65, 87, 139 |
| abstract_inverted_index.as | 27 |
| abstract_inverted_index.by | 104, 114, 131, 156 |
| abstract_inverted_index.in | 170, 207, 211 |
| abstract_inverted_index.is | 75, 112, 129, 167, 197, 203 |
| abstract_inverted_index.it | 196 |
| abstract_inverted_index.km | 152, 155 |
| abstract_inverted_index.of | 47, 124, 133, 150, 186, 192 |
| abstract_inverted_index.on | 57, 78, 100 |
| abstract_inverted_index.so | 35 |
| abstract_inverted_index.× | 153 |
| abstract_inverted_index.For | 0 |
| abstract_inverted_index.and | 5, 9, 18, 34, 42, 54, 84, 119, 183 |
| abstract_inverted_index.are | 63, 97, 146 |
| abstract_inverted_index.but | 209 |
| abstract_inverted_index.fit | 134 |
| abstract_inverted_index.its | 43 |
| abstract_inverted_index.not | 205 |
| abstract_inverted_index.on. | 36 |
| abstract_inverted_index.out | 169 |
| abstract_inverted_index.six | 92 |
| abstract_inverted_index.the | 66, 88, 108, 120, 140, 143, 161, 176, 187, 193, 200 |
| abstract_inverted_index.Then | 107 |
| abstract_inverted_index.also | 210 |
| abstract_inverted_index.city | 174 |
| abstract_inverted_index.data | 49, 67 |
| abstract_inverted_index.five | 116, 125 |
| abstract_inverted_index.have | 14, 24 |
| abstract_inverted_index.only | 206 |
| abstract_inverted_index.risk | 7, 40, 94, 165 |
| abstract_inverted_index.such | 26 |
| abstract_inverted_index.that | 199 |
| abstract_inverted_index.with | 136, 148, 190 |
| abstract_inverted_index.10.4. | 158 |
| abstract_inverted_index.based | 77, 99 |
| abstract_inverted_index.built | 76 |
| abstract_inverted_index.layer | 56 |
| abstract_inverted_index.model | 111, 122, 189, 202 |
| abstract_inverted_index.novel | 39 |
| abstract_inverted_index.other | 194 |
| abstract_inverted_index.power | 1, 58, 162 |
| abstract_inverted_index.shown | 198 |
| abstract_inverted_index.those | 191 |
| abstract_inverted_index.tower | 163 |
| abstract_inverted_index.under | 11, 60, 175 |
| abstract_inverted_index.ArcGIS | 157 |
| abstract_inverted_index.damage | 164 |
| abstract_inverted_index.layer, | 50, 53, 68, 91, 142 |
| abstract_inverted_index.method | 135 |
| abstract_inverted_index.models | 96, 128 |
| abstract_inverted_index.strong | 30 |
| abstract_inverted_index.system | 2, 45 |
| abstract_inverted_index.towers | 59 |
| abstract_inverted_index.value. | 20 |
| abstract_inverted_index.Chinese | 172 |
| abstract_inverted_index.carried | 168 |
| abstract_inverted_index.coastal | 173 |
| abstract_inverted_index.current | 22 |
| abstract_inverted_index.machine | 101 |
| abstract_inverted_index.models, | 195 |
| abstract_inverted_index.optimal | 110 |
| abstract_inverted_index.quality | 208 |
| abstract_inverted_index.results | 145 |
| abstract_inverted_index.spatial | 70 |
| abstract_inverted_index.studies | 23 |
| abstract_inverted_index.typhoon | 12, 61, 177 |
| abstract_inverted_index.unequal | 137 |
| abstract_inverted_index.weight. | 138 |
| abstract_inverted_index.However, | 21 |
| abstract_inverted_index.accuracy | 149 |
| abstract_inverted_index.combined | 121, 188, 201 |
| abstract_inverted_index.database | 74 |
| abstract_inverted_index.disaster | 3, 13, 62 |
| abstract_inverted_index.factors, | 29 |
| abstract_inverted_index.goodness | 132 |
| abstract_inverted_index.learning | 102 |
| abstract_inverted_index.problems | 25 |
| abstract_inverted_index.relative | 109 |
| abstract_inverted_index.results, | 160 |
| abstract_inverted_index.selected | 113 |
| abstract_inverted_index.superior | 127, 204 |
| abstract_inverted_index.comparing | 115, 180 |
| abstract_inverted_index.equipment | 79 |
| abstract_inverted_index.important | 15 |
| abstract_inverted_index.indicator | 185 |
| abstract_inverted_index.knowledge | 51, 89 |
| abstract_inverted_index.operation | 80 |
| abstract_inverted_index.predicted | 144, 181 |
| abstract_inverted_index.proposed. | 64 |
| abstract_inverted_index.quantity. | 212 |
| abstract_inverted_index.Therefore, | 37 |
| abstract_inverted_index.algorithms | 103 |
| abstract_inverted_index.assessment | 8, 41, 166 |
| abstract_inverted_index.consisting | 46, 123 |
| abstract_inverted_index.evaluation | 117 |
| abstract_inverted_index.extraction | 52, 90 |
| abstract_inverted_index.geographic | 85 |
| abstract_inverted_index.incomplete | 28 |
| abstract_inverted_index.prediction | 95 |
| abstract_inverted_index.prevention | 4 |
| abstract_inverted_index.relatively | 126 |
| abstract_inverted_index.scientific | 16 |
| abstract_inverted_index.similarity | 184 |
| abstract_inverted_index.visualized | 147 |
| abstract_inverted_index.complicated | 32 |
| abstract_inverted_index.engineering | 19 |
| abstract_inverted_index.established | 98, 130 |
| abstract_inverted_index.indicators, | 118 |
| abstract_inverted_index.information | 73 |
| abstract_inverted_index.intelligent | 93 |
| abstract_inverted_index.mitigation, | 6 |
| abstract_inverted_index.distribution | 182 |
| abstract_inverted_index.information, | 81, 83 |
| abstract_inverted_index.information. | 86 |
| abstract_inverted_index.multi-source | 71 |
| abstract_inverted_index.significance | 17 |
| abstract_inverted_index.calculations, | 33 |
| abstract_inverted_index.heterogeneous | 72 |
| abstract_inverted_index.optimization. | 106 |
| abstract_inverted_index.subjectivity, | 31 |
| abstract_inverted_index.visualization | 10, 44, 55, 141 |
| abstract_inverted_index.hyperparameter | 105 |
| abstract_inverted_index.meteorological | 82 |
| abstract_inverted_index.‘Mujigae’. | 178 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5052324373 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I196699116 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.83233192 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |