Developing national-scale basic guideline on flood-adaptation strategies under climate change using probabilistic and deterministic factors
Bibliographic record
Abstract
• Broad-scale classification is a useful guideline for establishing flood adaptation strategies. • Studies have predicted flood occurrences for 10 years using probabilistic and deterministic factors. • Rainfall and land use were identified as probabilistic and deterministic factors, respectively. • Cluster analysis was performed using these indices for 1795 municipalities in Japan. • Six clusters were obtained, each with a suitable basic adaptation strategy. As climate adaptation strategies against floods, implementing structural measures in damage-prone areas, supplemented by nonstructural measures (e.g., ecosystem-based disaster risk reduction (Eco-DRR)), is a viable approach. However, under climate change, predicting damage-prone areas is challenging, hindering the development of effective adaptation strategies. The increase in floods under climate change can be broadly attributed to probabilistic, triggerring hazards, and deterministic, inducing vulnerability factors. Therefore, quantification for levels of probabilistic and deterministic factors may establish adaptation strategies such as prioritize areas where structural measures should be implemented. Herein, we establish basic guideline for developing adaptation strategies against floods, considering probabilistic and deterministic factors simultaneously. We investigated all the municipalities in Japan and modeled flood occurrence from 2010 to 2019 based on government statistics, using the rainfall indicator as a probabilistic factors and terrain factor, which considers land use as a deterministic factor to decide appropriate indicators. Thereafter, we quantified the increase and decrease in rainfall indicator as probabilistic factor. Additionally, we used terrain factor, which considers current land use as a deterministic factor. We implemented nonhierarchical clustering using probabilistic and deterministic factors and classified 1795 municipalities in Japan into six clusters. The findings confirm the feasibility of developing specific adaptation strategies based on the clusters, such as strengthening the installation of artificial structures in areas belonging to the cluster in which floods expectedly increase and enhancing measures in clusters that remain unchanged based on flood histories.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".