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Updated: Mar 9, 2026

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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通过使用一致性指数和交叉验证的特征选择框架,为水质管理提供决策准备的可解释机器学习

Chao-Chin Chang1, Yuming Chen2, Chun-Yu Chen1

  • 1Department of Safety, Health and Environmental Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 824, Taiwan, ROC.

Environmental research
|March 7, 2026
PubMed
概括

本研究引入了一种一致性指数 (CI) 和递归特征消除 (RFECV),以提高可解释机器学习 (XML) 用于水质预测的可靠性. 这些方法提高了对环境监测和水资源管理决策的信任.

关键词:
一致性指数是一致性指数.决策方式 决策方式美国RFECV RFECV水资源管理水资源的管理.可以解释的机器学习

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