从数据到收获:利用集体机器学习,在气候变化期间在加拿大提高作物产量预测
Navid Mahdizadeh Gharakhanlou1, Liliana Perez1
1Laboratoire de Géosimulation Environnementale (LEDGE), Département de Géographie, Université de Montréal, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, QC H2V 0B3, Canada.
The Science of the total environment
|August 25, 2024
概括
集成机器学习模型准确预测加拿大的作物产量. XGBoost 卓越,蜜蜂种群是影响作物生产的最重要因素.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 环境科学 环境科学
背景情况:
- 准确的作物产量预测对于农业规划和政策至关重要.
- 集成机器学习 (ML) 模型为改善收益率预测提供了潜力,但在这个领域尚未得到充分探索.
研究的目的:
- 评估五种整体ML模型 (AdaBoost,GBM,XGBoost,LightGBM,RF) 用于预测加拿大的甘和大豆产量.
- 确定影响作物产量的关键因素,包括气候,土壤,植被和蜜蜂数据.
- 在气候变化情景下评估作物产量,并提供本地化产量图.
主要方法:
- 数据整合:气候变量,卫星衍生植被指数,土壤特征和蜜蜂普查数据被编制.
- 数据预处理:气候数据的插值和平均值,植被指数,土壤数据和蜜蜂数据用Python进行了组织.
- 模型评价:使用根平均平方误差 (RMSE),R平方和平均绝对误差 (MAE) 训练和评估了五个集合ML模型.
主要成果:
- XGBoost表现出最高的准确性,实现了最低的MAE和RMSE,以及最高的R平方值,用于甘露和大豆产量预测.
- 蜜蜂群落被确定为最有影响力的因素,显著影响了甘露 (52.34%) 和大豆 (57.18%) 的产量.
- 气候变化情景显示预测的变化很小,但表明对整体产量的潜在负面影响.
结论:
- 集团ML,特别是XGBoost,为加拿大准确的作物产量预测提供了一个强大的框架.
- 蜜蜂种群是作物生产率的关键驱动因素,强调了授粉者健康的重要性.
- 该研究为农业利益相关者提供了有价值的本地化作物产量地图和气候变化影响评估.
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