Application of Flood Modeling in Informal Settlement Areas in Makassar City, Indonesia
Bibliographic record
Abstract
The Spatial and Regional Plan (RTRW) of Makassar City 2015-2034 does not cover the Mariso and Mamajang Sub-districts in flood-prone areas, even though both districts have experienced flooding.To investigate this issue, this research focuses on flood modeling-based flood modeling.The aim of this research is to identify the existing spatial conditions in the research area by conducting flood simulation modeling in both districts and analyzing the spatial impact of flood modeling on informal settlement areas.The research was conducted over four months, from April to July 2023.The data used includes primary and secondary data obtained from government agencies and field observations.Spatial data includes actual flood areas, land cover, and DEM-NAS, while non-spatial data involves rainfall and tidal data.The research methods include qualitative and quantitative analyses.Spatial analysis is used to analyze the distribution of flood areas, elevation conditions, rainfall, land cover, informal settlement areas, and flood model maps.Meanwhile, quantitative analysis involves data analysis in tabulation and graphs, such as rainfall intensity, tidal data, Manning's roughness coefficient, runoff values, and the number of pixels in the flood model.The research results include four main pieces of information: an existing area analysis identifying 125 flood areas.The elevation of the coastal area is generally low, with the highest elevation on land reaching 23 meters.There are 11 types of land cover, and rainfall falls into the moderate to high category.Flood modeling results in macro and micro, simulations in terms of water levels and flood flow.Validation results show a modeling accuracy level of 69.03%.Meanwhile, the spatial impact of flood modeling results in 60 flood distribution areas with varying heights between 10 cm and 300 cm, with informal settlement areas behind the most affected.This research provides information to understand the flood characteristics in informal settlements in the Mariso and Mamajang Sub-districts of Makassar City through a comprehensive flood modeling-based approach.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".