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基于机器学习和贡献分析,评估净初级生产率变化和潜在的未来影响,在中国长江三角洲,中国

Qing Wei¹, Lianqing Xue², Zichen Jia¹

¹State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Ministry of Education Key Laboratory of Yangtze River Water Environment, Tongji University, Shanghai 200092, China.

The Science of the total environment

|June 13, 2025

概括

由于气候因素,长江三角洲的净初级生产率 (NPP) 每年都在增加. 未来的预测表明下降,人类活动显著影响了核电站的水平.

科学领域:

生态与环境科学生态与环境科学
气候变化研究气候变化研究
遥感和地理空间分析

背景情况:

净初级生产率 (NPP) 对于理解生态系统的碳捕获和植被动态,影响全球碳循环和气候变化减缓至关重要.
长江三角洲 (YRD) 等快速发展地区的时空空间核电站模式由于气候和人为因素的相互作用而复杂.

研究的目的:

从2001年到2020年,分析YRD中NPP的时空变化.
开发和验证一个先进的模型,用于准确的核电站估计和YRD的未来预测.

主要方法:

用于跨越2001-2020年核电站分析的多来源数据.
采用了由虫优化 (DBO) 算法优化的卷积神经网络 (CNN) 来进行核电站建模.
应用了沙普利增量解释 (SHAP) 和部分相关性分析,以确定关键影响因素.

主要成果:

经过DBO优化的CNN模型实现了高精度 (R2=0.914,RMSE=21.1 gC/m2/yr).
观察到平均NPP为0.795 KgC/年,每年显著增加3.72 gC/m2/yr.
确定了一个西北至东南的核电站梯度,并证实日照时间,海拔,降水和温度是主要的驱动因素.

结论:

气候因素主要推动了核电站的增长,而人类活动则导致了下降.
根据SSP情景 (SSP245和SSP585) 的未来核电站预测表明,从2030年到2050年,核电站的数量将逐渐下降.
该研究为YRD的碳管理策略提供了关键的见解.

关键词:

卷积神经网络是一种卷积神经网络.驱动力是驱动力的驱动力.净初级生产率净初级生产率时间空间模式的模式.长江三角洲的长江三角洲

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相关概念视频

Primary Production

Primary Production

The total amount of energy acquired by primary producers in an ecosystem is called gross primary production (GPP). However, of this energy, producers use some for metabolic processes, and some is lost as heat, decreasing the amount of energy available to the next trophic level. The remaining usable amount of energy is called the net primary productivity (NPP). In terrestrial ecosystems, NPP is driven by climate, while light penetration and nutrient availability drive NPP in aquatic ecosystems.