Flood Susceptibility Analysis Using Frequency Ratio Method in Walanae Watershed
Bibliographic record
Abstract
Flooding is a type of natural disaster that has occurred frequently in Indonesia since 2012-2022 and occupies the second highest position compared to other types of disasters released by the Indonesian National Disaster Management Agency.In South Sulawesi, flooding occurs every year, especially in areas affected by watersheds, such as the Walanae watershed.Indications of flooding causes include land change and overflows from rivers and lakes.Identifying factors affecting flood occurrence is necessary for the region's watershed management and development planning.This study was conducted to determine the factors that most influence the occurrence of floods and map the level of flood susceptibility in the Walanae watershed using the Frequency Ratio method.The causal factors analyzed in this study are rainfall, Topographic Wetness Index, elevation, slope, land cover, and distance from the river, which are then processed to obtain the frequency ratio value.The study results show that the most influential factor is land cover as a water-absorbing medium, with FR probability values of 4.27 and 3.31 in the water body and rice field cover classes.Land use direction needs to be followed up in the Walanae watershed, such as direction and correction of the spatial pattern plan improvements, especially in the spatial pattern of settlements and agricultural land that contribute to high flood impacts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".