基于联合学习的分布式作物田的参考蒸发透气估计
Muhammad Tausif1, Muhammad Waseem Iqbal1, Rab Nawaz Bashir2,3
1Department of Computer Science & Information Technology, The Superior University, Lahore, Punjab, Pakistan.
PloS one
|February 5, 2025
概括
联合学习增强了对不同地点的参考蒸发透气 (ETo) 估计. 随机森林回归模型实现了卓越的准确性,超过了可持续农业和水资源管理的当地模型.
科学领域:
- 环境科学 环境科学
- 农业科学 农业科学
- 数据科学数据科学数据科学
背景情况:
- 准确的参考蒸发透气 (ETO) 对水资源管理和可持续农业至关重要.
- 现有的机器学习模型用于ETO估计通常仅限于特定的地理区域.
- 集中式数据方法面临隐私和数据传输限制的挑战.
研究的目的:
- 提出一种联合学习方法,用于在多个不同天气条件的多个地点估计ETO.
- 评估不同机器学习模型 (RFR,SVR,DTR) 在ETO预测的联合学习框架中的性能.
- 通过特征重要性分析,确定影响ETO估计的关键天气参数.
主要方法:
- 实施了一个联合学习方法,使用来自巴基斯坦三个不同的地理位置的天气数据 (2012-2022).
- 在本地和全球范围内训练和评估随机森林回归器 (RFR),支持矢量回归器 (SVR) 和决策树回归器 (DTR) 模型.
- 进行特征重要性分析,以了解天气参数对模型性能的影响.
主要成果:
- 基于随机森林回归器 (RFR) 的联合学习模型表现出卓越的性能,达到R2 = 0.97%,RMSE = 0.44,MAE = 0.33 mm day-1,MAPE = 8.18%.
- 在每个选定的网站上,RFR联合模型的表现优于个别的本地机器学习模型.
- 最大温度和风速被确定为对ETO预测最有影响的因素.
结论:
- 联合学习提供了一种通用和保护隐私的方法,用于在各种气候条件下准确地估计ETO.
- 在联合学习框架中的RFR模型对于区域ETO预测非常有效.
- 了解特征的重要性有助于完善ETO估计模型,以改善水资源和农业管理.
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