Flood Hazard Analysis as Impact of Climate Change on Slum Areas in Palembang, South Sumatera
Bibliographic record
Abstract
Palembang and the surrounding areas there are many slums, especially alongside the river areas, the land is located in the wetlands area in Palembang. The growth of slums Village 5 Ulu Seberang Ulu I district, due to its strategic position in the region where the services and trade, thus becoming the choice of those newcomers to settle into the community and economic life along the river make migrants from villages to bring the crop to trade. From trading there are needs for a place to stay, and they set up lodges in a way ride with landlords and natives Palembang along the river. Eventually from makeshift cottage house, and began to grow houses on stilts. Because it’s near the center of trade and services, attracting residents urbanization outside of Palembang to choose to live in this village to find work with do not have the education and special skills, so many emerging slums without complete infrastructure housing is especially sanitation.In Palembang, floodsseems to have a tendency to increase every year. Increasing trend of flooding in Palembang not only the breadth of course, but the loss also increases as well. Materials used in the study was DEM, topographic maps, land use maps, maps tides, river flow data, the coefficient manning, cross section of the river and drainage system data. Value DEM manipulation, spatial patterns of river that flooded as a result of tidal depicted in the map indicates that the area is mostly in the form of alluvial land. Based onthe results ofa GIS analysis of the research region obtained five areas of flood hazard that area11.43% very high hazard, high hazard 8.71%, 5.99% medium hazard, low hazard 3.59%, 70.28% very low hazard, Where almost all areas of research into the danger area is very high, high, low and very low. Looking at the results that have been obtained through a process of spatial data processing almost the whole area along the river included in the criteria of high hazard is due to the use of land in the form of slums, the soil type is alluvial soil, and most drainage network density contrast is less well. Almost the entire District of Seberang Ulu I have a region surrounding the flat category.
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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 | 0.001 | 0.001 |
| 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".