Summary of Adopting Explainable-ai to Investigate the Impact Of Urban Morphology Design on Energy and Environmental Performance in Dry-arid Climates, by Pegah Eshraghi et al.
Adopting Explainable-AI to investigate the impact of urban morphology design on energy and environmental performance in dry-arid climates
by Pegah Eshraghi, Riccardo Talami, Arman Nikkhah Dehnavi, Maedeh Mirdamadi, Zahra-Sadat Zomorodian
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an innovative approach to evaluating and designing climate-resilient urban forms in dry arid-climates. By combining Urban Building Energy Modeling (UBEM) with machine learning methods and Explainable AI techniques, the study assesses the impact of 30 morphology parameters on energy metrics and environmental performance at the urban block level. The XGBoost model is found to be the most effective predictor, achieving high accuracy and a short training time. The study reveals that building shape, window-to-wall ratio, and commercial ratio are key factors affecting energy efficiency, while neighboring buildings’ heights and distances influence cooling demand and solar access. The research offers generalizable insights for other dry-arid regions and provides a scalable framework for developing climate-resilient urban designs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us design cities that use less energy and are more environmentally friendly. It uses special computer models to see how different city shapes and buildings affect energy usage and the environment. The researchers looked at 30 different factors that make up a city block, like building shape and window size, to figure out which ones matter most. They found that things like building height and distance from other buildings have a big impact on cooling demand and sunlight. This study can help us create more sustainable cities in dry areas around the world. |
Keywords
» Artificial intelligence » Machine learning » Xgboost
