Analisis Kerawanan Banjir Berbasis Sistem Informasi Geografis Sebagai Upaya Mitigasi Pada DAS Kedunggaleng Kabupaten Probolinggo
Bibliographic record
Abstract
DAS Kedunggaleng merupakan salah satu DAS di Wilayah Sungai Welang-Rejoso yang seringkali mengalami banjir akibat luapan sungai Kedunggaleng. Oleh sebab itu, diperlukan adanya peta rawan banjir untuk memberikan informasi mengenai bencana banjir pada DAS tersebut. Pemetaan daerah rawan banjir dilakukan melalui pemanfaatan Sistem Informasi Geografis (SIG) berdasarkan parameter hujan rancangan, kemiringan lereng, ketinggian lahan, jenis tanah, penggunaan lahan, dan kerapatan sungai. Berdasarkan hasil analisis, didapatkan bahwa daerah Sangat Tidak Rawan seluas 2,78 km2 (1%), Tidak Rawan seluas 90,30 km2 (33,8%), Sedang seluas 121,61 km2 (45,5%), Rawan seluas 30,58 km2 (11,5%) , dan Sangat Rawan seluas 21,72 km2 (8,1%) yang mendominasi daerah hilir DAS. Berdasarkan peta rawan banjir tersebut, dilakukan arahan mitigasi banjir melalui perencanaan embung kecil, kolan retensi, dan sistem peringatan dini. Hasil analisis menunjukkan bahwa terdapat tujuh buah embung kecil dan kolam retensi yang direncanakan dapat mereduksi volume pada saat debit puncak banjir sebesar 2,3% sampai 11,6% pada setiap sub-DAS embung dan 1,48% sampai 15,49% pada setiap catchment area kolam retensi. Kemudian, tingkan status sistem peringatan dini dilakukan berdasarkan tinggi muka air sungai dan didapatkan bahwa tinggi muka air pada kelas Normal 0,20 - 0,57 meter, Waspada 0,57 – 0,93 meter, Siaga 0,93 – 1,30 meter, Awas >1,30 meter.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".