Non-Linear Modelling of Land Surface Temperature Using Environmental Indices at Chattogram: A Comprehensive Study
Bibliographic record
Abstract
Abstract Global urbanisation is rapidly transforming city landscapes, intensifying land use changes, and contributing to rising land surface temperatures along with the Urban Heat Island (UHI) effect, a challenge that demands accurate prediction for effective climate adaptation. This study presents a comprehensive machine learning framework for modelling LST in Chattogram, Bangladesh, utilising an extensive 24-year Landsat time series. Six machine learning algorithms: Random Forest, Gradient Boosting, Extra Trees, radial-basis Support Vector Regression, multi-layer perception, and Ridge. Standard Scaler preceded SVR and MLP, which were rigorously tested on three key environmental indices: NDVI, NDBI, and MNDWI to predict LST. The evaluation employed robust time-series cross-validation to ensure meaningful performance assessment across all models. Results highlight that support vector regression (SVR) achieved notably superior predictive accuracy, outperforming linear methods by a significant margin. Intrinsic spectral indicators emerged as more effective for LST modelling than engineered features, demonstrating a remarkable capacity to reflect the underlying temperature dynamics. This work delivers a validated and practical LST prediction strategy, supporting sustainable urban planning and guiding climate resilience policy in rapidly developing areas like Chattogram.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".