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在堆肥过程中提高N₂O排放的预测,使用无模型的超级学习

Shuai Shi¹, Jiaxin Bao¹, Zhiheng Guo¹

¹College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China.

The Science of the total environment

|March 2, 2024

概括

模型无意识的元学习 (MAML) 准确地预测了来自肥堆肥的氧化 (N₂O) 排放. 该方法识别了诸如水分和氨等关键因素,有助于减少温室气体污染的战略.

科学领域:

环境科学环境科学
农业科学农业科学
机器学习机器学习

背景情况:

氧化 (N2O) 是一种强有力的温室气体,有助于臭氧层的消耗和全球变暖.
堆肥有机废物是有益的,但可以释放大量的N2O排放.
精确量化堆肥产生的N₂O排放对于减轻环境影响至关重要.

研究的目的:

开发和验证一种有效的机器学习模型,用于预测肥堆肥过程中的N₂O排放.
对其他机器学习方法进行模型不可知性元学习 (MAML) 方法的性能评估.
确定影响堆肥过程中N2O排放的关键因素.

主要方法:

采用了模型不可知的元学习 (MAML) 算法来预测N₂O排放.
将MAML性能与其他五种机器学习模型进行比较:反向传播神经网络,极端学习机器,ELM随机森林,梯度增强决策树和极端梯度增强.
进行了特征分析,以确定影响N₂O排放的最有影响的因素.

主要成果:

MAML模型实现了0.939的高R2值和18.42毫克d-1的低根平均平方误差 (RMSE).
与其他五种评估的机器学习方法相比,MAML表现优越.
结构材料的水分含量和氨度被确定为N₂O排放的最重要的预测因素.

结论:

MAML是一种高效的方法,用于预测便堆肥中的N₂O排放.
了解材料特性和工艺数据的影响是减轻N2O排放的关键.
这项研究为堆肥中N₂O排放预测和减少策略提供了一个强大的框架.

关键词:

机器学习模型的机器学习模型肥料堆肥是为了堆肥肥料.模型无意识的超级学习氧化氧化的使用方法预测预测预测

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