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多摄像头融合神经网络用于预测农田土壤中可用的含量

Ren-Jie Zhang¹, Shu-Fang Pan², Huai-Zeng Xing²

¹Longping Branch, College of Biology, Hunan University, Changsha, 410125, China; Key Lab of Prevention, Control and Remediation of Soil Heavy Metal Pollution, Hunan Institute of Agricultural Soil and Eco-Environment, Hunan Academy of Agricultural Sciences, Changsha, 410125, China; Key Laboratory of Agro-Environment in Midstream of Yangtze Plain, Ministry of Agriculture and Rural Affairs, Changsha, 410125, China.

Environmental pollution (Barking, Essex : 1987)

|October 9, 2025

概括

一个新的多图形融合神经网络通过整合空间地理数据,准确地预测土壤 (Cd) 度. 该模型改善了重金属污染预测,以有效地修复农田土壤.

科学领域:

环境科学环境科学
土壤科学土壤科学
机器学习机器学习

背景情况:

(Cd) 是农田土壤中的主要重金属污染物,对生态系统和人类健康构成风险.
准确预测可用的Cd度对于整治至关重要,但现有的模型往往忽略了可变的相互关系和空间效应.

研究的目的:

开发一种新的多图融合神经网络模型,用于预测土壤可用的Cd度.
将空间关系和地理因素 (海拔,矿山,道路,河流) 纳入Cd预测模型中.

主要方法:

提出了一种多图融合神经网络模型.
集成多个空间关系图表以捕捉地理因素的影响.
将模型性能与基线和单图模型进行比较.

主要成果:

多图形融合模型实现了高预测精度 (R2=0.82,RMSE=0.0338毫克千克-1,MAE=0.0249毫克千克-1).
在单图和基线模型中表现出优越的性能.
显示了较低的残余分布和更稳定的预测.

结论:

将地理因素的空间影响纳入其中显著提高了土壤可用Cd预测.
多图形融合模型为分析重金属污染空间异质性的环境驱动因素提供了有价值的工具.

关键词:

农业用地农业用地图表融合的图形融合的图形机器学习是机器学习.可用的土壤 Cd 空间效应是一种空间效应.

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