Predicting Long-term Urban Overheating and Their Mitigations from Nature Based Solutions Using Machine Learning and Field Measurements
Bibliographic record
Abstract
Urban overheating, exacerbated by climate change, threatens public health and urban sustainability. Traditional approaches, such as numerical simulations and field measurements, face challenges due to uncertainties in input data. This study integrates field measurements with machine learning models to predict the duration and severity of future urban overheating events, focusing on the role of urban greening under different global warming (GW) scenarios. Field measurements were conducted in summer 2024 at an office campus in Ottawa, a cold-climate city. Microclimate data were collected from four locations with varying levels of greenery: a large lawn without trees (Lawn), a parking lot without greenery (Parking), an area with sparsely distributed trees (Tree), and a fully covered forested area (Forest). Machine learning models, including Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks, were trained on local microclimate data, with LSTM achieving the best predictions. Four GW scenarios were analyzed, corresponding to different Shared Socioeconomic Pathways (SSP) for 2050 and 2090. Results show that the Universal Thermal Climate Index (UTCI) at the "Parking" location rises from about 27,\textdegree C under GW1.0 to 31,\textdegree C under GW3.5. Moreover, low health risk conditions (UTCI > 26,\textdegree C) increase across all locations due to climate change, regardless of greenery levels. However, tree-covered areas such as "Tree" and "Forest" effectively prevent extreme heat conditions (UTCI > 38.9,\textdegree C). These findings highlight the crucial role of urban greening in mitigating severe thermal stress and enhancing thermal comfort under future climate scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".