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Updated: May 17, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
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Published on: July 24, 2016

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使用机器学习分析EDHS-2019数据预测埃塞俄比亚未改善供水的决定因素

Jember Azanaw1, Mihret Melese2, Eshetu Abera Worede3

  • 1Department of Environmental and Occupational Health and Safety, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia. jemberazanaw21@gmail.com.

Scientific reports
|April 4, 2025
PubMed
概括

机器学习模型将社区贫困确定为埃塞俄比亚无法获得改善的水源的关键驱动因素. 针对贫困农村地区的有针对性的干预措施对于改善安全饮用水供应至关重要.

关键词:
2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年EDHS - 2019年功能重要性 功能重要性机器学习 机器学习随机的森林随机的森林空间分析是一种空间分析.没有改善的供水.

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