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Updated: Sep 16, 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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使用先进的机器学习模型预测广土壤中的土壤紧缩参数:一项比较研究

Sultan Almuaythir1, Muhammad Syamsul Imran Zaini2, Rida Hameed Lodhi3

  • 1Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia. s.alhomair@psau.edu.sa.

Scientific reports
|July 5, 2025
PubMed
概括

这项研究揭示了XG-Boost作为预测广土壤紧缩性质的顶级计算模型,这对于干旱地区的建筑至关重要. 它准确地预测最佳含水量和最大干密度,提高了项目的稳定性.

关键词:
压缩特性 压缩特性广的粘土土壤 广的粘土土壤机器学习方法的方法.预测模型的预测模型.

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