关于城市环境的时空图学习
Hewen Li1, Linlin Hou1, Jing Cui1
1State Key Laboratory of Urban-rural Water Resources and Environment, School of Eco-Environment, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China.
Environmental science & technology
|February 10, 2026
概括
时空图形学习 (STGL) 提供了一种新的方法来建模复杂的城市动态,改善环境智能和预测. 本综述综合了STGL在弹性和适应性城市系统方面的进展.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 城市规划 城市规划
背景情况:
- 城市环境表现出复杂的,涉及水,土壤,空气和基础设施的非线性动态.
- 传统的建模方法难以捕捉这些复杂的时空相互作用.
- 时空图形学习 (STGL) 为分析城市复杂性提供了一个强大的框架.
研究的目的:
- 为城市环境提供首个专门针对城市环境的时空图形学习 (STGL) 的全面审查.
- 综合最近在STGL方面的进展,包括图形构造,建模和融合策略.
- 检查STGL在各种城市系统和挑战中的多样化应用.
主要方法:
- 系统审查的空间时间图形学习 (STGL) 文献专注于城市应用.
- 分析图形构造技术,空间和时间建模方法以及数据融合策略.
- 在城市环境情报中突出STGL实施的案例研究检查.
主要成果:
- STGL有效地模拟了城市系统中的非线性,非欧几里德动态.
- 图形构造和建模方面的进步提高了预测准确性和决策支持.
- 在城市水,土壤,空气质量和风险管理方面成功应用.
结论:
- STGL是城市环境中环境智能的基础技术.
- 未来的方向包括联合学习,机器取消学习和用于增强STGL的元学习.
- 下一代STGL框架将支持更有弹性和适应性的城市环境.
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