机器学习组合,神经网络,混合和稀疏回归方法用于基于天气的雨水棉花产量预测
Girish R Kashyap1, Shankarappa Sridhara2, Konapura Nagaraja Manoj1
1Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India.
International journal of biometeorology
|April 27, 2024
概括
准确的棉花产量预测对利益相关者至关重要. 机器学习模型,特别是堆叠的通用集合和人工神经网络,通过分析天气数据提供可靠的预测,其中最低温度和湿度是关键因素.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 气象学 天气学
背景情况:
- 棉花是一种重要的经济作物,通常在雨天条件下种植,使得准确的产量预测对农民,工业和政策制定者至关重要.
- 在生长季节的天气模式显著影响最终的棉花产量.
- 传统的作物模拟模型可能很复杂;创新的大数据技术提供更快,更灵活的产量预测.
研究的目的:
- 为了证明机器学习 (ML) 算法的实用性,用于棉花产量预测.
- 为了比较各种ML模型在预测棉花产量的性能.
- 确定影响棉花产量的关键天气变量.
主要方法:
- 每周的天气指数被用作输入数据来模拟棉花产量.
- 不同的ML模型的性能使用标准来评估,例如正常化根平均平方误差 (nRMSE),平均绝对百分比误差 (MAPE) 和效率因子 (EF).
- 使用LASSO和ENET模型进行了变量重要性分析.
主要成果:
- 堆叠的通用集体和人工神经网络 (ANN) 模型表现出更高的性能,nRMSE和MAPE较低,效率更高.
- 最低温度和相对湿度被确定为所有地区棉花产量的主要决定因素.
- 建立了测试模型的性能排名,突出了堆叠的通用合奏和ANN作为表现最好的.
结论:
- 堆叠的通用合并和ANN方法对于可靠的棉花产量预测在地区或县级是有效的.
- 准确的产量预测为农业利益相关者提供了及时的决策.
- 机器学习方法为复杂的作物模拟模型提供了一个更容易获得和更有效的替代方案,用于产量预测.
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