提前珍珠小米产量预测:对印度拉贾斯坦邦的单个和整体机器学习方法的比较分析
Ahmad Alsaber1, Parul Setiya2, Anurag Satpathi3
1Department of Management, College of Business and Economics, American University of Kuwait, Salmiya, Kuwait.
PloS one
|March 11, 2025
概括
在印度拉贾斯坦邦,精确的珍珠小米产量预测使用机器学习得到了改进. 组合ELNET模型被推用于准确的预测,其性能优于其他单个和组合方法.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 气候科学 气候科学
背景情况:
- 珍珠小米 (Pennisetum glaucum L.) 是一个重要的,抗旱作物,对干旱和半干旱地区至关重要.
- 印度的拉贾斯坦邦是珍珠小米的主要生产国,面临着产量变化的挑战.
研究的目的:
- 提高印度拉贾斯坦邦九个地区的珍珠小米产量预测.
- 评估和排名各种单个和整体机器学习模型用于产量预测.
主要方法:
- 使用了23年的 (1997-2019) 珍珠小米产量和NASA POWER天气数据.
- 应用单个模型 (GLM,ELNET,XGB,SVR,RF) 和组合组合.
- 基于R2和nRMSE (%) 的排名模型用于培训和测试阶段.
主要成果:
- 模特的表现在各个地区各不相同; 组合模特在巴默和纳尔扎.
- 个别的GLM和XGB显示高校准,但验证不良,表明过拟合.
- 整体ELNET模型实现了最佳整体性能,其次是个别射频模型.
结论:
- 定制的机器学习模型选择对于准确的珍珠小米产量预测至关重要.
- 建议使用Ensemble ELNET来准确预测拉贾斯坦邦的产量.
- 了解模型过拟合对于可靠的农业预测至关重要.
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