Prediksi Perubahan Tutupan Lahan dan Suhu Permukaan Lahan
Bibliographic record
Abstract
Abstract. The city of Surabaya as a metropolitan city cannot be avoided from the phenomenon of urbanization. This has an impact on increasing built-up land which has the potential to erode other lands, one of which is vegetation land so that it can trigger an increase in land surface temperature. The existence of the Cellular Automata method and the use of satellite imagery make it possible to predict changes in land cover and land surface temperature in the future. It was found that in 2034 in the city of Surabaya there was a change in land cover in the form of an increase in built-up land from 25692,15 Ha to 29982,19 Ha, a decrease in vegetation land from 3615,78 Ha to 1698,45 Ha, a decrease in vacant land from 3138,87 Ha to 889,04 Ha, decreased water area from 402,14 Ha to 280,25 Ha and found an increase in land surface temperature which was dominated by temperature class >30 °C by 30044,38 Ha, decreased temperature class from 28-30 °C to 2171,83 Ha, a decrease in the temperature class from 24-26 °C to 117,56 Ha, a decrease in the temperature class from 22-24 °C to 4,52 Ha without a temperature class of 20-22 °C and <20 °C. Abstrak. Kota Surabaya sebagai kota metropolitan tidak terhindar dari adanya fenomena urbanisasi. Hal tersebut berdampak pada peningkatan lahan terbangun yang berpotensi mengikis lahan-lahan lain salah satunya lahan vegetasi sehingga dapat memicu peningkatan suhu permukaan lahan. Adanya metode Cellular Automata dan pemanfaatan citra satelit memungkinkan untuk memprediksi perubahan tutupan lahan dan suhu permukaan lahan di masa yang akan datang. Didapati hasil penelitian pada tahun 2034 di Kota Surabaya terjadi perubahan tutupan lahan berupa meningkatnya lahan terbangun dari 25692,15 Ha menjadi 29982,19 Ha, menurunnya lahan vegetasi dari 3615,78 Ha menjadi 1698,45 Ha, menurunnya lahan kosong dari 3138,87 Ha menjadi 889,04 Ha, menurunnya lahan perairan dari 402,14 Ha menjadi 280,25 Ha serta ditemui meningkatnya suhu permukaan lahan yang didominasi oleh kelas suhu >30 °C sebesar 30044,38 Ha, menurunnya kelas suhu 28-30 °C menjadi 2171,83 Ha, menurunnya kelas suhu 26-28 °C menjadi 510,65 Ha, menurunnya kelas suhu 24-26 °C menjadi 117,56 Ha, menurunnya kelas suhu 22-24 °C menjadi 4,52 Ha tanpa adanya kelas suhu 20-22 °C dan <20 °C.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".