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Updated: Jun 14, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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空间机器学习用于探索卢旺达北部省份社会经济,农业生态和气候特征的低高度对年龄的变化

Gilbert Nduwayezu1,2, Clarisse Kagoyire1,3, Pengxiang Zhao1

  • 1Department of Physical Geography and Ecosystem Science GIS Centre Lund University Lund Sweden.

GeoHealth
|September 5, 2024
PubMed
概括

在卢旺达,儿童发育迟缓影响了27%的儿童,特别是在Musanze,Gakenke和Gicumbi地区. 空间分析确定了关键的风险因素,如孤立,高度和降雨影响儿童成长.

关键词:
可解释的人工智能高度-对于-年龄.当地的非线性效应.空间机器学习 空间机器学习

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