混合贝叶斯深度学习模型用于预测非洲城市的城市热岛强度
D Lynda1, G Logeswari2, K Tamilarasi1
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, 600127, India.
Scientific reports
|August 25, 2025
概括
一个新的混合人工智能模型准确地预测了非洲的表面城市热岛 (SUHI) 强度,帮助气候适应. 这种工具有助于城市规划者减轻快速增长的城市的热量风险,提高城市的抵御力.
科学领域:
- 环境科学
- 气候科学
- 城市规划
背景情况:
- 城市热岛加剧了能源需求,空气质量问题和公共卫生风险,特别是在快速城市化的非洲地区.
- 非洲适应气候的基础设施有限,
研究的目的:
- 开发和验证一个混合预测模型,用于预测整个非洲的地表城市热岛 (SUHI) 强度.
- 为城市规划者提供一个减轻非洲快速增长城市热负荷的工具.
主要方法:
- 一个混合组合模型,将贝叶斯神经网络 (BNNs) 与注意力机制和梯度增强回归器 (GBR) 结合起来.
- 利用来自1万多个城市的全球气候和城市数据集,包括土地表面温度,土地使用和人口密度.
- 使用十倍交叉验证进行模型验证.
主要成果:
- 混合模型在结构化气候数据方面表现优于传统的深度学习方法 (CNN,LSTM).
- 平均绝对误差 (MAE) 为1.47°C,平均平方误差 (MSE) 为3.56°C2,R2得分为0.84.
- 与基线架构相比,在郊区的模型精度提高了12.3%,MAE降低了8.6%.
结论:
- 提出的不确定性意识和可解释的机器学习模型对气候适应战略有价值.
- 这种模式支持城市规划的努力,
- 有效的SUHI强度预测对于管理城市化对公共卫生和基础设施的热影响至关重要.
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