Enhancing Flood Management Plan (FMP) Through Integration Strategic Environmental Assessment (SEA) in Thailand: The Case of Ayutthaya
Bibliographic record
Abstract
Rapid urbanization, deforestation and disaster management system exacerbated the risk of flooding in Thailand. In 2011, Thailand had learned the lessons from its experience of the mega flood disaster that point to the requirement of better solutions to reduce damage to property and human life. This article presents the Strategic Environmental Assessment (SEA) and Flood Management Framework (SEAFMF) in the existing flood management plan. This study aims to investigate how SEA can be integrated into Thailand’s flood management plan using an appropriate flood management framework. The methodology includes content analysis of qualitative and quantitative data based on a review of existing research, interviews with relevant organizations, and focus group discussions. The results show that the strategic environmental assessment approach can be used in decision making regarding the adaptation framework. As the pilot case, is a partially integrated model, more effective SEA should be completely done by establishing the appropriate legal framework and authority to directly responsible. The results could provide example of integration SEA into FMP, adaptation to climate change, and disaster management for other areas.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".