统计和机器学习模型用于使用天气指数预测特定地点的作物产量
Ajith S1, Manoj Kanti Debnath2, Karthik R3
1Department of Agricultural Statistics, Uttar Banga Krishi Viswavidyalaya, Cooch Behar, India. ajithagristat@gmail.com.
International journal of biometeorology
|August 31, 2024
概括
准确的作物产量预测需要整合天气数据. 非线性机器学习模型,如支持向量回归 (SVR) 和人工神经网络 (ANN),在特定位置的预测方面表现出卓越的性能.
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:
- 预测作物产量对于农业利益相关者来说至关重要.
- 农作物发展与天气变量密切相关,需要气象数据的整合.
- 气候产出关系表现出显著的局部变化,有利于地区级建模.
研究的目的:
- 系统地审查统计和机器学习模型,以利用天气因素预测作物产量.
- 确定常用和高性能模型用于位置和特定作物的产量预测.
- 探索不同建模方法的适用性,以捕捉复杂的气候产量相互作用.
主要方法:
- 统计和机器学习模型的系统文献综述.
- 用气象数据分析常用于作物产量预测的模型.
- 基于报告的成功率和适用性的模型性能评估.
主要成果:
- 人工神经网络 (ANN) 和多重线性回归是最常用的模型.
- 支持向量回归 (SVR) 显示出高成功率,在各种应用中表现良好.
- 非线性模型,特别是SVR和ANN,表现优于其他模型,表明天气和作物产量之间的复杂非线性关系.
结论:
- 像SVR和ANN这样的非线性机器学习模型对于作物产量预测非常有效.
- 模型优化,包括SVR中的超参数调整和ANN中的激活功能/神经元,提高了预测准确性.
- 综合优化统计和机器学习技术的地区级建模对于精确的,特定位置的作物产量预测至关重要.
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