Evaluating the Impacts of Natural Based Soluations on Long-term Urban Overheating through Machine Learning and Field Measurements
Bibliographic record
Abstract
Urban overheating has become a global issue, exacerbated by climate change and that may lead to severe effects on both public health as well as urban sustainability. This study is intended to permit the prediction of the longevity and severity of future urban overheating events by integrating field measurements and machine learning models, focusing on the impact of urban greening under different global warming (GW) scenarios. Field measurements have been conducted during summer 2024 in an office campus at Ottawa, a city located in cold climate zone. Microclimate data were measured at four locations within the campus, the four locations have different types and coverage levels of urban greenings – large lawn area without trees (Lawn), parking lot without any greening (Parking), greenery area with sparsely distributed trees (Tree) and an area with 100% coverage of trees (Forest). Models, such as Artificial Neural Networks (ANN), and Recurrent Neural Networks (RNN), and Long Short-Term Memory network models (LSTM) were trained on local microclimate data, with LSTM chosen for its superior performance predictions. Four Global Warming (GW) scenarios were considered to represent different Shared Socioeconomic Pathways (SSP) by 2050 and 2090. The results show that the UTCI at the “Parking” location increased from around 27 °C under GW1.0 to 31 °C under GW3.5. Besides, low health risk (UTCI > 26 °C) will be increased in all locations due to climate change impacts, regardless of urban greening conditions. However, the tree area like 'Tree' and 'Forest' are effective in eliminating the occurrence of extremely high-risk heat conditions (UTCI > 38.9 °C). The findings demonstrate that urban greening plays a crucial role in reducing severe thermal stress, thereby enhancing thermal comfort under future climate scenarios.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".