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Updated: Jun 7, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
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Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

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一个基于机器学习和SHAP解释技术的可解释 (可解释) 模型,用于绘制风侵蚀危险的地图

Hamid Gholami1, Ehsan Darvishi2, Navazollah Moradi2

  • 1Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas,, Hormozgan, Iran. hadesertt64@gmail.com.

Environmental science and pollution research international
|November 15, 2024
PubMed
概括

这项研究使用可解释机器学习 (ML) 和沙普利增材扩展 (SHAP) 技术绘制风侵蚀危险图. 高度和土壤散装密度被确定为关键因素,SVM模型在危险分类中显示出高准确度.

关键词:
选择功能选择功能选择.可解释 (可解释) 的ML模型伊朗 伊朗 伊朗 伊朗排名算法排名的算法在 SHAP 技术中,使用 SHAP 技术.风力侵蚀造成的侵蚀.

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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon09:44

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