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涂料材料对缓释尿素超颗粒 (USG) 的酸盐浸出的影响的比例影响预测模型使用机器学习和RSM技术
Sidhartha Sekhar Swain1, Tapan Kumar Khura1, Pramod Kumar Sahoo1
1Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
Scientific reports
|February 6, 2024
概括
先进的机器学习模型准确地预测酸盐出,优化化肥料使用和减少地下水污染. 响应表面方法 (RSM) 证明最有效,为有效的肥料管理提供了指导方针.
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:
- 酸盐出会影响肥料效率和地下水质量.
- 传统的建模算法难以准确地预测酸盐出.
- 先进的机器学习为精确的建模提供了一个有前途的解决方案.
研究的目的:
- 用各种机器学习算法预测和优化酸盐出.
- 评估支持矢量机,人工神经网络,随机森林,M5树,减少错误修剪树和响应表面方法的性能.
- 确定最佳的肥料涂层参数,以尽量减少酸盐出.
主要方法:
- 使用的土壤柱与尿素超颗粒 (USG) 和不同的涂层.
- 采用先进的机器学习算法,包括RSM,SVM,ANN,RF,M5P和REPTree.
- 使用RMSE,MAE和R平方等统计参数评估模型性能.
主要成果:
- 响应表面方法 (RSM) 在预测不同USG涂层的酸盐漏方面表现出卓越的性能.
- 鉴定了尼姆油/白油,粘土/硫和年龄的特定组合,以尽量减少酸盐漏.
- RSM为USG涂层提供了最佳参数,显著减少了酸盐出.
结论:
- 机器学习,特别是RSM,为准确的酸盐漏预测提供了强大的工具.
- 研究结果为优化肥料应用和减轻环境污染提供了实际指导方针.
- 这项研究有助于政策制定者和研究人员选择适合可持续农业的预测工具.
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