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Record W1995664367 · doi:10.5539/jsd.v8n1p218

Flood Hazard Analysis as Impact of Climate Change on Slum Areas in Palembang, South Sumatera

2015· article· en· W1995664367 on OpenAlexvenueno aff
Ana Heryana, Dwi Setyawan, Budhi Setiawan, Dadang Hikmah Purnama

Bibliographic record

VenueJournal of Sustainable Development · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyUrbanizationSanitationFlooding (psychology)SlumHuman settlementHazardFlood mythWork (physics)WetlandWater resource managementEnvironmental sciencePopulationEconomic growthEnvironmental engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.270
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2015
Admission routes1
Has abstractyes

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