ANALYSIS OF THE URBAN HEAT ISLAND USING MICROCLIMATE SIMULATION FOR URBAN QUARTER
Bibliographic record
Abstract
This study demonstrates that the development of green infrastructure is an important task in the formation of ur-ban planning strategies to reduce the effect of the urban heat island and improve the ecosystem of the city. Simulation is an effective method for studying the complex mechanisms of urban climate formation at the stage of urban planning. The purpose of this study is to assess the severity of the urban heat island in relation to the quarter, taking into ac-count various scenarios of its landscaping, using modern simulation tools. Based on modeling in the ENVI-met soft-ware and computing complex of the thermal conditions of the quarter on the hottest days, its high thermal heterogenei-ty was established. The maximum temperature values are noted in roads and soils, the minimum – in green areas. The temperature conditions of the quarter changes over time. Exceeding the average temperature of the urban quarter ter-ritory over the average temperature in green areas means the possibility of forming an urban heat island. The calcula-tion established that most of the quarter is located in the urban heat island zone. This is also confirmed by an increase in air temperature in an urbanized area. Recommendations are given to mitigate the urban heat island. Based on the results of the thermal simulation of the quarter, it was established that the most effective solution compared to the orig-inal model is an increase in the area of lawn grass and shrubs by 10%, an increase in the area of trees by 12% and a decrease in asphalt pavements of paths and sites by 5.7%. Such a solution maximizes the mitigation of the urban heat island and provides a high level of comfort for the urban environment. Further research will focus on the development of a multi-factor correlation-regression model to assess the mitigation effect of urban heat islands by means of improv-ing green infrastructure.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".