Prediksi Kerentanan Kekeringan Perkotaan Menggunakan Machine Learning: Pendekatan untuk Perencanaan Kota Tangguh di Kota Kupang
Bibliographic record
Abstract
The city of Kupang in East Nusa Tenggara faces increasingly serious drought challenges due to climate change and rapid urbanization, prompting the need for this study to predict drought vulnerability in the region. This study aims to develop a drought vulnerability prediction model using a machine learning approach that combines Normalized Difference Drought Index (NDDI) data, built-up areas, and population data in the last five (5) years. The methods used include NDDI calculations from satellite imagery, a zonal statistical analysis, and drought vulnerability simulations using models such as Random Forest, Support Vector Machine, and Artificial Neural Network. The results show that the Random Forest model provides the best prediction with the highest R Squared value, and indicates an increased risk of drought in several villages in 2030 and 2040, especially in areas with rapid population growth and expansion of built-up areas. The conclusion of this study confirms that the developed approach is able to provide a more accurate picture of drought-prone areas, so that it can be an important guide in more resilient and sustainable urban planning, and recommends strengthening water management and spatial planning policies with early intervention in the most vulnerable areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".