A machine learning approach to deal with ambiguity in the humanitarian decision‐making Article Swipe
Emilia Graß
,
Janosch Ortmann
,
Burcu Balcik
,
Walter Rei
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1111/poms.14018
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1111/poms.14018
One of the major challenges for humanitarian organizations in response planning is dealing with the inherent ambiguity and uncertainty in disaster situations. The available information that comes from different sources in postdisaster settings may involve missing elements and inconsistencies, which can hamper effective humanitarian decision‐making. In this paper, we propose a new methodological framework based on graph clustering and stochastic optimization to support humanitarian decision‐makers in analyzing the implications of divergent estimates from multiple data sources on final decisions and efficiently integrating these estimates into decision‐making. To the best of our knowledge, the integration of ambiguous information into decision‐making by combining a cluster machine learning method with stochastic optimization has not been done before. We illustrate the proposed approach on a realistic case study that focuses on locating shelters to serve internally displaced people (IDP) in a conflict setting, specifically, the Syrian civil war. We use the needs assessment data from two different reliable sources to estimate the shelter needs in Idleb, a district of Syria. The analysis of data provided by two assessment sources has indicated a high degree of ambiguity due to inconsistent estimates. We apply the proposed methodology to integrate divergent estimates in making shelter location decisions. The results highlight that our methodology leads to higher satisfaction of demand for shelters than other approaches such as a classical stochastic programming model. Moreover, we show that our solution integrates information coming from both sources more efficiently thereby hedging against the ambiguity more effectively. With the newly proposed methodology, the decision‐maker is able to analyze the degree of ambiguity in the data and the degree of consensus between different data sources to ultimately make better decisions for delivering humanitarian aid.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/poms.14018
- https://journals.sagepub.com/doi/pdf/10.1111/poms.14018
- OA Status
- hybrid
- Cited By
- 9
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366490164
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4366490164Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1111/poms.14018Digital Object Identifier
- Title
-
A machine learning approach to deal with ambiguity in the humanitarian decision‐makingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-20Full publication date if available
- Authors
-
Emilia Graß, Janosch Ortmann, Burcu Balcik, Walter ReiList of authors in order
- Landing page
-
https://doi.org/10.1111/poms.14018Publisher landing page
- PDF URL
-
https://journals.sagepub.com/doi/pdf/10.1111/poms.14018Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://journals.sagepub.com/doi/pdf/10.1111/poms.14018Direct OA link when available
- Concepts
-
Ambiguity, Computer science, Operations research, Cluster analysis, Humanitarian aid, Linear programming, Management science, Machine learning, Economics, Engineering, Algorithm, Economic growth, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 5, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
67Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4366490164 |
|---|---|
| doi | https://doi.org/10.1111/poms.14018 |
| ids.doi | https://doi.org/10.1111/poms.14018 |
| ids.openalex | https://openalex.org/W4366490164 |
| fwci | 4.21942914 |
| type | article |
| title | A machine learning approach to deal with ambiguity in the humanitarian decision‐making |
| biblio.issue | 9 |
| biblio.volume | 32 |
| biblio.last_page | 2974 |
| biblio.first_page | 2956 |
| topics[0].id | https://openalex.org/T11502 |
| topics[0].field.id | https://openalex.org/fields/14 |
| topics[0].field.display_name | Business, Management and Accounting |
| topics[0].score | 0.9991999864578247 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1407 |
| topics[0].subfield.display_name | Organizational Behavior and Human Resource Management |
| topics[0].display_name | Facility Location and Emergency Management |
| topics[1].id | https://openalex.org/T10747 |
| topics[1].field.id | https://openalex.org/fields/33 |
| topics[1].field.display_name | Social Sciences |
| topics[1].score | 0.9510999917984009 |
| topics[1].domain.id | https://openalex.org/domains/2 |
| topics[1].domain.display_name | Social Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3312 |
| topics[1].subfield.display_name | Sociology and Political Science |
| topics[1].display_name | Disaster Management and Resilience |
| topics[2].id | https://openalex.org/T11464 |
| topics[2].field.id | https://openalex.org/fields/36 |
| topics[2].field.display_name | Health Professions |
| topics[2].score | 0.9348999857902527 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/3600 |
| topics[2].subfield.display_name | General Health Professions |
| topics[2].display_name | Homelessness and Social Issues |
| is_xpac | False |
| apc_list.value | 2580 |
| apc_list.currency | USD |
| apc_list.value_usd | 2580 |
| apc_paid.value | 2580 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 2580 |
| concepts[0].id | https://openalex.org/C2780522230 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8604050278663635 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1140419 |
| concepts[0].display_name | Ambiguity |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6815649271011353 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C42475967 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5988048911094666 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q194292 |
| concepts[2].display_name | Operations research |
| concepts[3].id | https://openalex.org/C73555534 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5823518633842468 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q622825 |
| concepts[3].display_name | Cluster analysis |
| concepts[4].id | https://openalex.org/C521897407 |
| concepts[4].level | 2 |
| concepts[4].score | 0.42428573966026306 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q826745 |
| concepts[4].display_name | Humanitarian aid |
| concepts[5].id | https://openalex.org/C41045048 |
| concepts[5].level | 2 |
| concepts[5].score | 0.41889703273773193 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q202843 |
| concepts[5].display_name | Linear programming |
| concepts[6].id | https://openalex.org/C539667460 |
| concepts[6].level | 1 |
| concepts[6].score | 0.4011431336402893 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2414942 |
| concepts[6].display_name | Management science |
| concepts[7].id | https://openalex.org/C119857082 |
| concepts[7].level | 1 |
| concepts[7].score | 0.31899046897888184 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[7].display_name | Machine learning |
| concepts[8].id | https://openalex.org/C162324750 |
| concepts[8].level | 0 |
| concepts[8].score | 0.14891329407691956 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[8].display_name | Economics |
| concepts[9].id | https://openalex.org/C127413603 |
| concepts[9].level | 0 |
| concepts[9].score | 0.13108724355697632 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[9].display_name | Engineering |
| concepts[10].id | https://openalex.org/C11413529 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[10].display_name | Algorithm |
| concepts[11].id | https://openalex.org/C50522688 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q189833 |
| concepts[11].display_name | Economic growth |
| concepts[12].id | https://openalex.org/C199360897 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[12].display_name | Programming language |
| keywords[0].id | https://openalex.org/keywords/ambiguity |
| keywords[0].score | 0.8604050278663635 |
| keywords[0].display_name | Ambiguity |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6815649271011353 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/operations-research |
| keywords[2].score | 0.5988048911094666 |
| keywords[2].display_name | Operations research |
| keywords[3].id | https://openalex.org/keywords/cluster-analysis |
| keywords[3].score | 0.5823518633842468 |
| keywords[3].display_name | Cluster analysis |
| keywords[4].id | https://openalex.org/keywords/humanitarian-aid |
| keywords[4].score | 0.42428573966026306 |
| keywords[4].display_name | Humanitarian aid |
| keywords[5].id | https://openalex.org/keywords/linear-programming |
| keywords[5].score | 0.41889703273773193 |
| keywords[5].display_name | Linear programming |
| keywords[6].id | https://openalex.org/keywords/management-science |
| keywords[6].score | 0.4011431336402893 |
| keywords[6].display_name | Management science |
| keywords[7].id | https://openalex.org/keywords/machine-learning |
| keywords[7].score | 0.31899046897888184 |
| keywords[7].display_name | Machine learning |
| keywords[8].id | https://openalex.org/keywords/economics |
| keywords[8].score | 0.14891329407691956 |
| keywords[8].display_name | Economics |
| keywords[9].id | https://openalex.org/keywords/engineering |
| keywords[9].score | 0.13108724355697632 |
| keywords[9].display_name | Engineering |
| language | en |
| locations[0].id | doi:10.1111/poms.14018 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S149070780 |
| locations[0].source.issn | 1059-1478, 1937-5956 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1059-1478 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Production and Operations Management |
| locations[0].source.host_organization | https://openalex.org/P4310320595 |
| locations[0].source.host_organization_name | Wiley |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320595 |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | https://journals.sagepub.com/doi/pdf/10.1111/poms.14018 |
| 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 | Production and Operations Management |
| locations[0].landing_page_url | https://doi.org/10.1111/poms.14018 |
| locations[1].id | pmh:oai:ub-madoc.bib.uni-mannheim.de:64699 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4377196315 |
| 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 | MADOC (University of Mannheim) |
| locations[1].source.host_organization | https://openalex.org/I177802217 |
| locations[1].source.host_organization_name | University of Mannheim |
| locations[1].source.host_organization_lineage | https://openalex.org/I177802217 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | acceptedVersion |
| locations[1].raw_type | NonPeerReviewed |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | |
| locations[2].id | pmh:oai:econstor.eu:10419/288143 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306401696 |
| 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 | Econstor (Econstor) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].source.host_organization_lineage | |
| locations[2].license | cc-by-nc-nd |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | doc-type:article |
| locations[2].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://hdl.handle.net/10419/288143 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5007275023 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8460-8395 |
| authorships[0].author.display_name | Emilia Graß |
| authorships[0].countries | DE |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I177802217 |
| authorships[0].affiliations[0].raw_affiliation_string | Business School, University of Mannheim, Mannheim, Germany |
| authorships[0].institutions[0].id | https://openalex.org/I177802217 |
| authorships[0].institutions[0].ror | https://ror.org/031bsb921 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I177802217 |
| authorships[0].institutions[0].country_code | DE |
| authorships[0].institutions[0].display_name | University of Mannheim |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Emilia Grass |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Business School, University of Mannheim, Mannheim, Germany |
| authorships[1].author.id | https://openalex.org/A5066064145 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-3811-6047 |
| authorships[1].author.display_name | Janosch Ortmann |
| authorships[1].countries | CA |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I159129438, https://openalex.org/I4210133247 |
| authorships[1].affiliations[0].raw_affiliation_string | GERAD, CRM, and Department of Analytics, Operations and IT, Université du Québec á Montréal, Montréal, Quebec, Canada |
| authorships[1].institutions[0].id | https://openalex.org/I4210133247 |
| authorships[1].institutions[0].ror | https://ror.org/02pkvpx84 |
| authorships[1].institutions[0].type | facility |
| authorships[1].institutions[0].lineage | https://openalex.org/I108192572, https://openalex.org/I159129438, https://openalex.org/I4210133247, https://openalex.org/I45683168, https://openalex.org/I49663120, https://openalex.org/I5023651 |
| authorships[1].institutions[0].country_code | CA |
| authorships[1].institutions[0].display_name | Group for Research in Decision Analysis |
| authorships[1].institutions[1].id | https://openalex.org/I159129438 |
| authorships[1].institutions[1].ror | https://ror.org/002rjbv21 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I159129438, https://openalex.org/I49663120 |
| authorships[1].institutions[1].country_code | CA |
| authorships[1].institutions[1].display_name | Université du Québec à Montréal |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Janosch Ortmann |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | GERAD, CRM, and Department of Analytics, Operations and IT, Université du Québec á Montréal, Montréal, Quebec, Canada |
| authorships[2].author.id | https://openalex.org/A5069820239 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3575-1846 |
| authorships[2].author.display_name | Burcu Balcik |
| authorships[2].countries | TR |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I44925452 |
| authorships[2].affiliations[0].raw_affiliation_string | Industrial Engineering Department, Ozyegin University, Istanbul, Turkey |
| authorships[2].institutions[0].id | https://openalex.org/I44925452 |
| authorships[2].institutions[0].ror | https://ror.org/01jjhfr75 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I44925452 |
| authorships[2].institutions[0].country_code | TR |
| authorships[2].institutions[0].display_name | Özyeğin University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Burcu Balcik |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Industrial Engineering Department, Ozyegin University, Istanbul, Turkey |
| authorships[3].author.id | https://openalex.org/A5088880981 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-6626-8251 |
| authorships[3].author.display_name | Walter Rei |
| authorships[3].countries | CA |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I159129438 |
| authorships[3].affiliations[0].raw_affiliation_string | CIRRELT, and Department of Analytics, Operations and IT, Université du Québec á Montréal, Montréal, Quebec, Canada |
| authorships[3].institutions[0].id | https://openalex.org/I159129438 |
| authorships[3].institutions[0].ror | https://ror.org/002rjbv21 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I159129438, https://openalex.org/I49663120 |
| authorships[3].institutions[0].country_code | CA |
| authorships[3].institutions[0].display_name | Université du Québec à Montréal |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Walter Rei |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | CIRRELT, and Department of Analytics, Operations and IT, Université du Québec á Montréal, Montréal, Quebec, Canada |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://journals.sagepub.com/doi/pdf/10.1111/poms.14018 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A machine learning approach to deal with ambiguity in the humanitarian decision‐making |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11502 |
| primary_topic.field.id | https://openalex.org/fields/14 |
| primary_topic.field.display_name | Business, Management and Accounting |
| primary_topic.score | 0.9991999864578247 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1407 |
| primary_topic.subfield.display_name | Organizational Behavior and Human Resource Management |
| primary_topic.display_name | Facility Location and Emergency Management |
| related_works | https://openalex.org/W2353179089, https://openalex.org/W2923538289, https://openalex.org/W2353125546, https://openalex.org/W2470643824, https://openalex.org/W2349635380, https://openalex.org/W4353089801, https://openalex.org/W2353819554, https://openalex.org/W2359488321, https://openalex.org/W2389866386, https://openalex.org/W2905116230 |
| cited_by_count | 9 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 5 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 2 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1111/poms.14018 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S149070780 |
| best_oa_location.source.issn | 1059-1478, 1937-5956 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1059-1478 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Production and Operations Management |
| best_oa_location.source.host_organization | https://openalex.org/P4310320595 |
| best_oa_location.source.host_organization_name | Wiley |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320595 |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | https://journals.sagepub.com/doi/pdf/10.1111/poms.14018 |
| 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 | Production and Operations Management |
| best_oa_location.landing_page_url | https://doi.org/10.1111/poms.14018 |
| primary_location.id | doi:10.1111/poms.14018 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S149070780 |
| primary_location.source.issn | 1059-1478, 1937-5956 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1059-1478 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Production and Operations Management |
| primary_location.source.host_organization | https://openalex.org/P4310320595 |
| primary_location.source.host_organization_name | Wiley |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320595 |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | https://journals.sagepub.com/doi/pdf/10.1111/poms.14018 |
| 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 | Production and Operations Management |
| primary_location.landing_page_url | https://doi.org/10.1111/poms.14018 |
| publication_date | 2023-04-20 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2003156116, https://openalex.org/W3165607881, https://openalex.org/W2046520163, https://openalex.org/W3031065629, https://openalex.org/W3178109043, https://openalex.org/W2090074247, https://openalex.org/W2946719591, https://openalex.org/W3047863327, https://openalex.org/W2988749387, https://openalex.org/W1569990960, https://openalex.org/W6629510986, https://openalex.org/W3110977802, https://openalex.org/W3037539437, https://openalex.org/W2067510068, https://openalex.org/W3119506958, https://openalex.org/W2572001128, https://openalex.org/W2116509768, https://openalex.org/W2056345094, https://openalex.org/W3011681177, https://openalex.org/W1986370907, https://openalex.org/W3184825017, https://openalex.org/W3208893139, https://openalex.org/W1908730295, https://openalex.org/W2235915772, https://openalex.org/W2768302825, https://openalex.org/W2345172237, https://openalex.org/W2211606247, https://openalex.org/W1990119044, https://openalex.org/W3116607398, https://openalex.org/W2011802426, https://openalex.org/W2959559174, https://openalex.org/W1980849679, https://openalex.org/W2078004651, https://openalex.org/W2789884919, https://openalex.org/W207991364, https://openalex.org/W25662807, https://openalex.org/W4229369233, https://openalex.org/W2508611272, https://openalex.org/W3163606624, https://openalex.org/W3170112077, https://openalex.org/W2143258383, https://openalex.org/W2755912278, https://openalex.org/W1964762688, https://openalex.org/W2297338375, https://openalex.org/W2916279744, https://openalex.org/W2913901754, https://openalex.org/W6891872824, https://openalex.org/W1987971958, https://openalex.org/W4226049201, https://openalex.org/W2121947440, https://openalex.org/W4200188259, https://openalex.org/W2013578513, https://openalex.org/W2516138216, https://openalex.org/W2024316885, https://openalex.org/W1883415534, https://openalex.org/W3040427204, https://openalex.org/W2779303070, https://openalex.org/W2972482489, https://openalex.org/W3154583681, https://openalex.org/W329177768, https://openalex.org/W2059710803, https://openalex.org/W3027146252, https://openalex.org/W2132914434, https://openalex.org/W3104390259, https://openalex.org/W3042744203, https://openalex.org/W2168595459, https://openalex.org/W2009086942 |
| referenced_works_count | 67 |
| abstract_inverted_index.a | 50, 101, 120, 136, 162, 177, 219 |
| abstract_inverted_index.In | 45 |
| abstract_inverted_index.To | 86 |
| abstract_inverted_index.We | 114, 144, 186 |
| abstract_inverted_index.as | 218 |
| abstract_inverted_index.by | 99, 171 |
| abstract_inverted_index.in | 8, 19, 30, 65, 135, 160, 195, 260 |
| abstract_inverted_index.is | 11, 252 |
| abstract_inverted_index.of | 1, 69, 89, 94, 164, 168, 180, 210, 258, 266 |
| abstract_inverted_index.on | 55, 76, 119, 126 |
| abstract_inverted_index.to | 61, 129, 155, 183, 191, 207, 254, 272 |
| abstract_inverted_index.we | 48, 225 |
| abstract_inverted_index.One | 0 |
| abstract_inverted_index.The | 22, 166, 200 |
| abstract_inverted_index.and | 17, 37, 58, 79, 263 |
| abstract_inverted_index.can | 40 |
| abstract_inverted_index.due | 182 |
| abstract_inverted_index.for | 5, 212, 277 |
| abstract_inverted_index.has | 109, 175 |
| abstract_inverted_index.may | 33 |
| abstract_inverted_index.new | 51 |
| abstract_inverted_index.not | 110 |
| abstract_inverted_index.our | 90, 204, 228 |
| abstract_inverted_index.the | 2, 14, 67, 87, 92, 116, 140, 146, 157, 188, 241, 246, 250, 256, 261, 264 |
| abstract_inverted_index.two | 151, 172 |
| abstract_inverted_index.use | 145 |
| abstract_inverted_index.With | 245 |
| abstract_inverted_index.able | 253 |
| abstract_inverted_index.aid. | 280 |
| abstract_inverted_index.been | 111 |
| abstract_inverted_index.best | 88 |
| abstract_inverted_index.both | 234 |
| abstract_inverted_index.case | 122 |
| abstract_inverted_index.data | 74, 149, 169, 262, 270 |
| abstract_inverted_index.done | 112 |
| abstract_inverted_index.from | 27, 72, 150, 233 |
| abstract_inverted_index.high | 178 |
| abstract_inverted_index.into | 84, 97 |
| abstract_inverted_index.make | 274 |
| abstract_inverted_index.more | 236, 243 |
| abstract_inverted_index.show | 226 |
| abstract_inverted_index.such | 217 |
| abstract_inverted_index.than | 214 |
| abstract_inverted_index.that | 25, 124, 203, 227 |
| abstract_inverted_index.this | 46 |
| abstract_inverted_index.war. | 143 |
| abstract_inverted_index.with | 13, 106 |
| abstract_inverted_index.(IDP) | 134 |
| abstract_inverted_index.apply | 187 |
| abstract_inverted_index.based | 54 |
| abstract_inverted_index.civil | 142 |
| abstract_inverted_index.comes | 26 |
| abstract_inverted_index.final | 77 |
| abstract_inverted_index.graph | 56 |
| abstract_inverted_index.leads | 206 |
| abstract_inverted_index.major | 3 |
| abstract_inverted_index.needs | 147, 159 |
| abstract_inverted_index.newly | 247 |
| abstract_inverted_index.other | 215 |
| abstract_inverted_index.serve | 130 |
| abstract_inverted_index.study | 123 |
| abstract_inverted_index.these | 82 |
| abstract_inverted_index.which | 39 |
| abstract_inverted_index.Idleb, | 161 |
| abstract_inverted_index.Syria. | 165 |
| abstract_inverted_index.Syrian | 141 |
| abstract_inverted_index.better | 275 |
| abstract_inverted_index.coming | 232 |
| abstract_inverted_index.degree | 179, 257, 265 |
| abstract_inverted_index.demand | 211 |
| abstract_inverted_index.hamper | 41 |
| abstract_inverted_index.higher | 208 |
| abstract_inverted_index.making | 196 |
| abstract_inverted_index.method | 105 |
| abstract_inverted_index.model. | 223 |
| abstract_inverted_index.paper, | 47 |
| abstract_inverted_index.people | 133 |
| abstract_inverted_index.against | 240 |
| abstract_inverted_index.analyze | 255 |
| abstract_inverted_index.before. | 113 |
| abstract_inverted_index.between | 268 |
| abstract_inverted_index.cluster | 102 |
| abstract_inverted_index.dealing | 12 |
| abstract_inverted_index.focuses | 125 |
| abstract_inverted_index.hedging | 239 |
| abstract_inverted_index.involve | 34 |
| abstract_inverted_index.machine | 103 |
| abstract_inverted_index.missing | 35 |
| abstract_inverted_index.propose | 49 |
| abstract_inverted_index.results | 201 |
| abstract_inverted_index.shelter | 158, 197 |
| abstract_inverted_index.sources | 29, 75, 154, 174, 235, 271 |
| abstract_inverted_index.support | 62 |
| abstract_inverted_index.thereby | 238 |
| abstract_inverted_index.analysis | 167 |
| abstract_inverted_index.approach | 118 |
| abstract_inverted_index.conflict | 137 |
| abstract_inverted_index.disaster | 20 |
| abstract_inverted_index.district | 163 |
| abstract_inverted_index.elements | 36 |
| abstract_inverted_index.estimate | 156 |
| abstract_inverted_index.inherent | 15 |
| abstract_inverted_index.learning | 104 |
| abstract_inverted_index.locating | 127 |
| abstract_inverted_index.location | 198 |
| abstract_inverted_index.multiple | 73 |
| abstract_inverted_index.planning | 10 |
| abstract_inverted_index.proposed | 117, 189, 248 |
| abstract_inverted_index.provided | 170 |
| abstract_inverted_index.reliable | 153 |
| abstract_inverted_index.response | 9 |
| abstract_inverted_index.setting, | 138 |
| abstract_inverted_index.settings | 32 |
| abstract_inverted_index.shelters | 128, 213 |
| abstract_inverted_index.solution | 229 |
| abstract_inverted_index.Moreover, | 224 |
| abstract_inverted_index.ambiguity | 16, 181, 242, 259 |
| abstract_inverted_index.ambiguous | 95 |
| abstract_inverted_index.analyzing | 66 |
| abstract_inverted_index.available | 23 |
| abstract_inverted_index.classical | 220 |
| abstract_inverted_index.combining | 100 |
| abstract_inverted_index.consensus | 267 |
| abstract_inverted_index.decisions | 78, 276 |
| abstract_inverted_index.different | 28, 152, 269 |
| abstract_inverted_index.displaced | 132 |
| abstract_inverted_index.divergent | 70, 193 |
| abstract_inverted_index.effective | 42 |
| abstract_inverted_index.estimates | 71, 83, 194 |
| abstract_inverted_index.framework | 53 |
| abstract_inverted_index.highlight | 202 |
| abstract_inverted_index.indicated | 176 |
| abstract_inverted_index.integrate | 192 |
| abstract_inverted_index.realistic | 121 |
| abstract_inverted_index.approaches | 216 |
| abstract_inverted_index.assessment | 148, 173 |
| abstract_inverted_index.challenges | 4 |
| abstract_inverted_index.clustering | 57 |
| abstract_inverted_index.decisions. | 199 |
| abstract_inverted_index.delivering | 278 |
| abstract_inverted_index.estimates. | 185 |
| abstract_inverted_index.illustrate | 115 |
| abstract_inverted_index.integrates | 230 |
| abstract_inverted_index.internally | 131 |
| abstract_inverted_index.knowledge, | 91 |
| abstract_inverted_index.stochastic | 59, 107, 221 |
| abstract_inverted_index.ultimately | 273 |
| abstract_inverted_index.efficiently | 80, 237 |
| abstract_inverted_index.information | 24, 96, 231 |
| abstract_inverted_index.integrating | 81 |
| abstract_inverted_index.integration | 93 |
| abstract_inverted_index.methodology | 190, 205 |
| abstract_inverted_index.programming | 222 |
| abstract_inverted_index.situations. | 21 |
| abstract_inverted_index.uncertainty | 18 |
| abstract_inverted_index.effectively. | 244 |
| abstract_inverted_index.humanitarian | 6, 43, 63, 279 |
| abstract_inverted_index.implications | 68 |
| abstract_inverted_index.inconsistent | 184 |
| abstract_inverted_index.methodology, | 249 |
| abstract_inverted_index.optimization | 60, 108 |
| abstract_inverted_index.postdisaster | 31 |
| abstract_inverted_index.satisfaction | 209 |
| abstract_inverted_index.organizations | 7 |
| abstract_inverted_index.specifically, | 139 |
| abstract_inverted_index.methodological | 52 |
| abstract_inverted_index.decision‐maker | 251 |
| abstract_inverted_index.inconsistencies, | 38 |
| abstract_inverted_index.decision‐makers | 64 |
| abstract_inverted_index.decision‐making | 98 |
| abstract_inverted_index.decision‐making. | 44, 85 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5007275023 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I177802217 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.92024117 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |