Assessing the multidimensional nature of flood and drought vulnerability index: A systematic review of literature
Bibliographic record
Abstract
Vulnerability to floods and droughts is a complex and multidimensional phenomenon influenced by various factors. This systematic review paper focuses on communities’ vulnerability to floods and droughts. It presents an overview of the current knowledge on the topic, including definitions and conceptual frameworks related to vulnerability. The study synthesizes existing literature from various disciplines, including hydrology, climatology, geography, and social sciences, to identify key factors contributing to vulnerability and its impacts on communities, infrastructure, and ecosystems. Through a comprehensive analysis of 83 articles published between 2010 and 2023, this paper identifies themes, methodologies, and knowledge gaps in flood and drought vulnerability assessment. The findings reveal that vulnerability to floods and droughts depends on a range of factors, including physical exposure, socioeconomic status, governance, and cultural values. Most of the published articles have focused on regional-scale studies. There has been an increase in the number of vulnerability studies addressing this issue after 2019. Among the various methods analyzed, min-max normalization (52 % of articles) and equal weighting (27 %) were the most frequently used data normalization and aggregation methods. However, the paper identifies a significant research gap in the lack of sensitivity analysis or validation of the indices developed based on the most common parameters, such as population density, gender, income, and precipitation levels. It also emphasizes the need for true transdisciplinary approaches for a comprehensive assessment of flood and drought vulnerabilities. The systematic review concludes with a synthesis of core vulnerability indicators and recommendations for future research and policy directions aimed at reducing the vulnerability of communities to these natural hazards. • Vulnerability to flood and drought is multifaceted and influenced by various factors. • The review combines knowledge from across disciplines to better understand vulnerability. • Findings reveal key factors contributing to vulnerability and research gaps. • Vulnerability indices must include sensitivity analyses as part of their deployment. • Vulnerability must be considered through a transdisciplinary lens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".