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使用随机森林和元启发优化算法预测谷物压缩实验中的孔隙性

Jiahao Chen^1,2, Jiaxin Li¹, Deqian Zheng^1,2,3

¹College of Civil Engineering Henan University of Technology Zhengzhou China.

Food science & nutrition

|March 31, 2025

概括

机器学习准确地预测了仓库中的谷物堆的孔隙性. 图尼卡特集群算法-随机森林模型提供了高效的,分层的多孔性评估,对于防止存储损失至关重要.

科学领域:

农业工程农业工程
机器学习应用机器学习应用
食品储存科学食品储存科学

背景情况:

长期的谷物储存面临着由于凝结,菌和昆虫侵袭而造成的重大损失,特别是在大型大仓库中.
谷物堆的多孔性是影响储存谷物的热/湿转移和通风效率的关键因素.
准确的孔径评估对于减轻储存损失和确保粮食安全至关重要.

研究的目的:

为了研究大宗谷物堆在公寓仓库中的孔隙分布模式.
开发和评估用于预测谷物堆多孔性的机器学习模型.
确定最佳的机器学习模型,以高效,准确地预测孔径.

主要方法:

进行了压缩实验,以收集用于毛孔性预测的数据.
开发了五种机器学习模型:随机森林 (RF) 和四种混合模型 (PSO-RF,GWO-RF,SCA-RF,TSA-RF).
用错误分析,泰勒图,评估指标和多标准评估来评估模型性能.

主要成果:

混合机器学习模型显著优于标准的随机森林模型.
衣集团算法-随机森林 (TSA-RF) 模型实现了最高的预测准确性 (R2=0.9923训练,R2=0.9723测试).
层次预测显示,谷物多孔度在中心较高,并且随着深度的增加而向边缘下降.

结论:

TSA-RF模型提供了一种新的,高效的方法,用于预测散装储存中的谷物多孔性.
了解孔隙分布有助于优化通风和防止谷物仓库中的腐烂.
这种机器学习方法可以快速进行孔隙性评估,从而有助于改善粮食安全.

关键词:

公寓式仓库仓库压缩实验是一个压缩实验.谷物多孔性谷物多孔性超启发式优化算法超启发式优化算法随机的森林随机的森林

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