一种混合机器学习模型用于预测农业生产成本:在埃及整合经济敏感性分析和环境因素
Shimaa Barakat1, Heba I Elkhouly2, Amr Sofey3
1Department of Electrical Engineering, Beni-Suef University, Beni-Suef, Egypt.
Journal of environmental management
|June 29, 2025
概括
本研究介绍了一种混合机器学习模型,用于预测埃及农业生产成本. 支持矢量机 (SVM) 模型实现了卓越的准确性,识别了关键的成本驱动因素,并为粮食安全政策提供了信息.
科学领域:
- 农业经济学 农业经济学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 准确预测农业生产成本对于埃及的可持续发展至关重要.
- 生产力对经济和环境波动敏感.
- 现有的模型缺乏经济和环境因素的全面整合.
研究的目的:
- 开发一种混合机器学习模型,以提高埃及的农业成本预测.
- 整合时间序列预测和经济敏感性分析.
- 为政策干预提供可操作的见解.
主要方法:
- 利用了关于番茄生产的20年 (2004-2023) 的数据集.
- 开发了一种混合模型,将支持矢量机 (SVM),随机森林 (RF) 和决策树 (DT) 与时间序列预测集成在一起.
- 在考虑环境因素的情况下进行了经济敏感性分析.
主要成果:
- 在三个生长季节中,SVM模型表现出卓越的性能,比RF和DT提高了2%的R平方.
- 确定了人类劳动工资,灌用水成本和最低温度作为关键成本驱动因素.
- 时间序列预测预测通货膨胀和燃料价格的上;灵敏度分析强调了它们显著的经济影响.
结论:
- 综合混合模型提供了更准确和可操作的农业成本预测.
- 调查结果支持积极的政策干预,以应对预期的经济趋势并增强农业的性.
- 这种方法对埃及和非洲的粮食安全和可持续性有影响.
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