将基于深度学习的替代模型集成到城市森林分配优化中,以最大限度地节制碳
Da Seul Kim1, Dong Kun Lee2, Eun Sub Kim3
1Department of Landscape Architecture and Rural System Engineering, Seoul National University, Seoul, Republic of Korea.
Journal of environmental management
|January 12, 2026
概括
使用深度学习框架优化城市植树,最大限度地减少碳排放. 现有森林附近的战略种植可以提高净初级生产率 (NPP) 和气候缓解的景观连接性.
科学领域:
- 城市生态学 城市生态学
- 气候变化缓解减缓 气候变化缓解减缓
- 地理空间分析是什么?
背景情况:
- 城市植树对于碳捕获至关重要,但目前的战略往往忽视空间变化和生态联系.
- 扩大面积的目标可能不是最大化碳吸收的最有效方法.
研究的目的:
- 开发和应用城市植林的综合优化框架.
- 在城市环境中最大限度地提高净初级生产率 (NPP) 和碳封存.
- 通过战略绿色基础设施规划,加强生态连接.
主要方法:
- 开发了一个深度学习代理模型,将人工神经网络 (ANN) 和遗传算法 (GA) 结合起来.
- 训练了ANN的地形,气候,土地使用和景观变量,以预测NPP.
- 使用了SHapley添加式扩展 (SHAP) 来确定因素的重要性和GA来优化植树区.
主要成果:
- 该ANN模型准确地预测了NPP (测试数据上的R2=0.82).
- 接近现有森林被确定为核电站增强的关键因素.
- GA优化表明,绿地附近的植树造林通过改善连通性显著提升了核电站.
结论:
- 这种数据驱动的框架有效地优化了城市森林的碳捕获.
- 森林植被的战略定位,优先考虑生态连接,比简单的面积扩张更有效.
- 这种方法支持适应性城市绿色基础设施规划,以实现碳中和的未来.
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