A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Examining temporally varying nonlinear effects of urban form on urban heat island using explainable machine learning: A case of Seoul
https://orcid.org/0000-0001-9841-4823
Abstract Many empirical studies have examined the relationship between urban form and canopy layer urban heat island (CUHI). However, these studies mostly have used small sample sizes and statistical methods that assume CUHI responds linearly to urban form features. Additionally, the differences in the effect of urban form on CUHI at different time instances during the diurnal cycle have not received sufficient attention. To address these issues, this study employs a relatively large sample of 1,058 microclimate sensors in Seoul, Korea, and analyzes CUHI at 9 a.m., 3 p.m., and 9 p.m. on a typical summer day. Gradient boosting decision tree (GBDT) based regression model and linear regression model and their variants with spatial information were constructed and compared for each time instance. Results show that spatially explicit GBDT models had the highest accuracy. Feature importance analysis shows that built form factors such as mean building height were more important during the afternoon, while surface fractions such as road surface fraction had greater influences during the morning and nighttime. Partial dependence plots (PDPs) show that urban form features influence CUHI in a complex nonlinear manner that varies for each time instance. PDPs were further scrutinized based on their activation and threshold effects. GBDT model findings directionally aligned with linear regression, but they provided nuanced insights into the form-CUHI relationship. These findings help planners to better understand the complexity of urban climate and intervention required to reduce CUHI magnitude across the diurnal cycle.
Highlights Nonlinearity between urban form and canyon urban heat island (CUHI) is significant. Spatially explicit gradient boosting predicted CUHI with highest accuracy. Nonlinearity and importance of urban form features vary during the diurnal cycle. Policymaking regarding CUHI should consider nonlinearity and temporal variation. Randomness in machine learning models can impact interpretability results.
Examining temporally varying nonlinear effects of urban form on urban heat island using explainable machine learning: A case of Seoul
https://orcid.org/0000-0001-9841-4823
Abstract Many empirical studies have examined the relationship between urban form and canopy layer urban heat island (CUHI). However, these studies mostly have used small sample sizes and statistical methods that assume CUHI responds linearly to urban form features. Additionally, the differences in the effect of urban form on CUHI at different time instances during the diurnal cycle have not received sufficient attention. To address these issues, this study employs a relatively large sample of 1,058 microclimate sensors in Seoul, Korea, and analyzes CUHI at 9 a.m., 3 p.m., and 9 p.m. on a typical summer day. Gradient boosting decision tree (GBDT) based regression model and linear regression model and their variants with spatial information were constructed and compared for each time instance. Results show that spatially explicit GBDT models had the highest accuracy. Feature importance analysis shows that built form factors such as mean building height were more important during the afternoon, while surface fractions such as road surface fraction had greater influences during the morning and nighttime. Partial dependence plots (PDPs) show that urban form features influence CUHI in a complex nonlinear manner that varies for each time instance. PDPs were further scrutinized based on their activation and threshold effects. GBDT model findings directionally aligned with linear regression, but they provided nuanced insights into the form-CUHI relationship. These findings help planners to better understand the complexity of urban climate and intervention required to reduce CUHI magnitude across the diurnal cycle.
Highlights Nonlinearity between urban form and canyon urban heat island (CUHI) is significant. Spatially explicit gradient boosting predicted CUHI with highest accuracy. Nonlinearity and importance of urban form features vary during the diurnal cycle. Policymaking regarding CUHI should consider nonlinearity and temporal variation. Randomness in machine learning models can impact interpretability results.
Examining temporally varying nonlinear effects of urban form on urban heat island using explainable machine learning: A case of Seoul
https://orcid.org/0000-0001-9841-4823
Abstract Many empirical studies have examined the relationship between urban form and canopy layer urban heat island (CUHI). However, these studies mostly have used small sample sizes and statistical methods that assume CUHI responds linearly to urban form features. Additionally, the differences in the effect of urban form on CUHI at different time instances during the diurnal cycle have not received sufficient attention. To address these issues, this study employs a relatively large sample of 1,058 microclimate sensors in Seoul, Korea, and analyzes CUHI at 9 a.m., 3 p.m., and 9 p.m. on a typical summer day. Gradient boosting decision tree (GBDT) based regression model and linear regression model and their variants with spatial information were constructed and compared for each time instance. Results show that spatially explicit GBDT models had the highest accuracy. Feature importance analysis shows that built form factors such as mean building height were more important during the afternoon, while surface fractions such as road surface fraction had greater influences during the morning and nighttime. Partial dependence plots (PDPs) show that urban form features influence CUHI in a complex nonlinear manner that varies for each time instance. PDPs were further scrutinized based on their activation and threshold effects. GBDT model findings directionally aligned with linear regression, but they provided nuanced insights into the form-CUHI relationship. These findings help planners to better understand the complexity of urban climate and intervention required to reduce CUHI magnitude across the diurnal cycle.
Highlights Nonlinearity between urban form and canyon urban heat island (CUHI) is significant. Spatially explicit gradient boosting predicted CUHI with highest accuracy. Nonlinearity and importance of urban form features vary during the diurnal cycle. Policymaking regarding CUHI should consider nonlinearity and temporal variation. Randomness in machine learning models can impact interpretability results.
Examining temporally varying nonlinear effects of urban form on urban heat island using explainable machine learning: A case of Seoul
Bansal, Parth (author) / Quan, Steven Jige (author)
Building and Environment ; 247
2023-10-18
Article (Journal)
Electronic Resource
English
Using explainable machine learning to understand how urban form shapes sustainable mobility
| DataCite | 2022
Examining the impact of Green Riyadh Project on alleviating urban heat island effects
| Springer Verlag | 2023