在南非使用集成建模进行灌适用性的微量测绘
Kudzai S Mpakairi1, Timothy Dube2, Mbulisi Sibanda3
1Institute for Water Studies, Faculty of Natural Sciences, University of the Western Cape, Cape Town, South Africa. kudzishaun@gmail.com.
Scientific reports
|October 21, 2025
概括
这项研究使用了集体机器学习来绘制南非灌适用性的地图,确定了农业发展的关键领域,以促进环境变化中的粮食安全.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 由于人口增长和环境变化,粮食不安全是南非的一个重大挑战.
- 有效的灌对于粮食和水安全以及实现可持续发展目标至关重要.
- 南非面临着重大的农业挑战,需要创新的解决方案.
研究的目的:
- 在南非各地确定和评估适合灌的地区.
- 为可持续农业规划制定一个强大的框架.
- 为提高粮食安全提供有针对性的灌开发信息.
主要方法:
- 采用了一个整体建模方法,结合了随机森林,极端梯度增强 (XGBoost) 和梯度增强机器 (GBM) 算法.
- 机器学习模型使用耕地存在/伪缺席数据和各种预测变量进行训练.
- 使用加权平均方法将单个模型的性能与组合模型相结合.
主要成果:
- 整体模型实现了高性能 (TSS为0.66,AUC为0.90),表现优于单个模型.
- 土地利用,人口密度和海拔被确定为灌适用性的最有影响的因素.
- 北开普省和西开普省被确定为适合灌的最大面积.
结论:
- 整体建模方法为南非的可持续灌规划提供了一个数据驱动的框架.
- 有针对性的灌开发可以提高农业生产率,并有助于粮食安全.
- 这项研究支持有效的资源配置和农业部门的经济发展.
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