Prediction of urban heat island intensity based on multiple linear regression and deep learning
Xinran Liu, Xia Zhu, Yuanping Liu, Cui Jia, Jie Cao, Yu Zhong, Yu Zhang, Yunjie Zhang, Lingye Yao, Lingye Yao, Lingye Yao, Lingye Yao · 2025
Read Prediction of urban heat island intensity based on multiple linear regression and deep learning by Xinran Liu, Xia Zhu, Yuanping Liu, Cui Jia, Jie Cao, Yu Zhong, Yu Zhang, Yunjie Zhang, Lingye Yao, Lingye Yao, Lingye Yao, Lingye Yao (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12747439.txt.
Description
Xinran Liu and co-authors forecast city heat in a 2025 study on the urban heat island effect. Fast city growth changes land use and makes built areas hotter than the land around them. That extra heat harms comfort and health, so knowing where it will grow has clear value.
A deep learning model was built to predict four land indices drawn from satellite data. A land change model then projected how land use will shift. A simpler math model linked the four indices to heat strength, with a fair fit to real data. Joining the two gives a map of future heat strength and spread for planners.
Catalog record
| CREATOR | Xinran Liu, Xia Zhu, Yuanping Liu, Cui Jia, Jie Cao, Yu Zhong, Yu Zhang, Yunjie Zhang, Lingye Yao, Lingye Yao, Lingye Yao, Lingye Yao |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 71 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |