使用分类交换机器学习方法提高农业土壤的氧化排放预测
Facundo Lussich1, Ryan Ackett1, Jashanjeet Kaur Dhaliwal1
1Department of Biosystems Engineering and Soil Science, University of Tennessee, Knoxville, Tennessee, USA.
Journal of environmental quality
|December 17, 2025
概括
一个新的类交换机器学习模型通过使用两个随机森林模型来准确预测氧化 (N2O) 排放,用于背景和热时刻流. 与农业系统中的传统模型相比,这种方法显著提高了预测准确性.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 机器学习 机器学习
背景情况:
- 准确预测氧化 (N2O) 排放对于气候智能农业至关重要.
- 现有的模型难以捕捉农业系统中N2O流动的复杂动态.
- 区分背景和短暂的高排放事件是改进建模的关键.
研究的目的:
- 介绍和评估一个新的集体机器学习模型",Class-Swap",用于预测N2O流.
- 为了比较类交换模型与传统随机森林 (RF) 模型的性能.
- 评估模型在不同的农业管理实践下预测N2O排放的能力.
主要方法:
- 开发了一个类交换模型,使用两个独立的射频模型训练了背景和热时刻N2O排放.
- 采用统计异常检测算法来对流量观测进行模型选择.
- 在从长期棉花作物旋转实验中获得的独立数据集上评估模型性能.
主要成果:
- 类交换模型显著优于传统的射频模型,显示较高的R2值 (0.33-0.34对比0.08-0.25).
- 类交换实现了较低的根平均平方误差 (9.8-9.9对10.5-11.6gN2O-N ha-1天-1).
- 该模型准确地捕获了辐射大小和时间动态,与传统的射频模型不同.
结论:
- 区分背景和热时刻N2O排放的生物地球化学驱动因素可以提高预测建模的准确性.
- 类交换方法为预测农业中的动态土壤N2O流提供了一个强大的替代方案.
- 建议对各种农业生态系统和条件进行进一步的验证.
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