Enhancing Urban Flood Resilience: The Role and Influence of Socio-Economics in the Chao Phraya River Basin, Thailand
Bibliographic record
Abstract
Thailand has the social and economic development policies since 1960s that causes the migration, land change, and population dispersion.The policy effects led to the urbanization in the Chao Phraya River Basin (CPRB) catchment areas, and conceptually synergizes with the water-related disasters by Climate Change.This research aims to highlight the role and influence of urban socio-economic factors on sensitive areas, flood risk exposure, and flood impact in the CPRB.This examination is a multiple-scale analysis based on the Driver-Pressure-State-Impact-Response Framework using data from government agencies.The collected data is utilized in mixed quantitative methods: Principal Component Analysis, Multiple Linear Regression, and K-Means Clustering.The district is a unit of analysis to represent the Meso-level.These data analyses are operated with 17 variables from 295 districts: municipal population, commercial values, water consumption, flood frequency, affected households, and economic losses.As a result, the analysis confirms that urban socioeconomics is necessary for urban expansion into flooded areas, especially the Bangkok Metropolitan Region.This expansion can enhance the urban flood risk and impact the local residences and commercials, especially the traditional town and lower-income communities.The finding implies the social and economic adaptive capacity-building requirement for balancing public infrastructure, compensation and funding mechanisms, and institutionalization. Financial measures to support capacity building are necessary.In conclusion, this scrutiny can lead to a strategic resilience framework essential in policy implication.This framework should include social adaptive capacity building, financial funding, and compensation mechanisms in traditional towns and lower-income communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".