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The Soil Ecosystem

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Updated: Jul 1, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

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Published on: October 16, 2018

学习与理解:什么时候人工智能在土壤有机碳预测中超越了基于过程的建模?

Luca G Bernardini¹, Christoph Rosinger², Gernot Bodner³

¹FFoQSI, Technopark 1/Haus D, 3430 Tulln an der Donau, Austria.

New biotechnology

|March 10, 2024

概括

机器学习 (ML) 模型擅长用大型数据集预测土壤有机碳 (SOC). 然而,基于过程的模型仍然优于长期生态研究中常见的小型数据集.

关键词:

人工智能的人工智能是人工智能.对模型性能进行比较.机器学习算法机器学习算法模特组合组合的模型组合.建模建模模型是什么基于过程的模型基于过程的模型.土壤有机碳土壤有机碳

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Last Updated: Jul 1, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

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科学领域:

生态建模生态建模
土壤科学土壤科学
机器学习应用程序机器学习应用程序

背景情况:

机器学习 (ML) 算法越来越多地用于生态建模.
对于ML在预测土壤有机碳 (SOC) 中使用长期研究典型的小数据集进行有限的评估.
对于SOC预测的ML性能并没有与传统的基于过程的模型进行比较.

研究的目的:

为了比较ML算法,基于过程的模型和SOC预测合集的性能.
用奥地利五个长期实验场所的数据来评估模型性能.
评估数据集大小和交叉验证策略对ML性能的影响.

主要方法:

基于过程的ML算法 (随机森林,具有多项式内核的支持向量机器),校准和未校准的基于过程的模型和集合的比较.
利用来自奥地利五个长期实验地点的256个独立数据点的数据.
应用了一个站点的交叉验证和减少训练样本大小,以测试模型的稳定性.

主要成果:

在使用所有可用的数据时,ML方法 (随机森林,SVM) 超过了基于过程的模型.
随着培训数据的减少或离开一个站点的交叉验证,ML算法性能显著下降.
基于过程的模型准确性高度依赖于适当的校准和模型组合.

结论:

当有大量数据集可用时,ML模型对于SOC预测是优越的.
基于过程的模型更适合探索SOC动态的潜在生物物理和生物化学机制.
建议使用结合了ML算法和基于过程的模型的集体来利用这两种方法的优势.