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Updated: Jun 24, 2025

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

Published on: October 16, 2018

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走向更绿色的未来:基于SVR的CO2预测模型,由SCMSSA算法增强

Oluwatayomi Rereloluwa Adegboye1, Afi Kekeli Feda2, Ephraim Bonah Agyekum3

  • 1Engineering Management Department, University of Mediterranean Karpasia, Mersin-10, Turkey.

Heliyon
|June 7, 2024
PubMed
概括

本研究引入了一种增强的Salp Swarm算法 (SCMSSA),用于更快,更准确的二氧化碳预测. 由SCMSSA改进的支持矢量回归模型实现了95%的准确性,确定了可持续性的关键排放因素.

关键词:
混乱的扰动是一种混乱的扰动.镜像成像策略的镜像策略.萨尔普群群算法 (SCMSSA) 是一个sinus 和 cosine 的扰动.支持向量的回归 (SVR)

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