基于先进机器学习的Kharif玉米在半干旱气候中的蒸发透气估计
Malkhan Singh Jatav1, A Sarangi2, D K Singh2
1Division of Agricultural Engineering, ICAR-IARI, New Delhi 110012, India
概括
机器学习模型准确地估计了半干旱地区玉米作物的每日蒸发透气量 (ETc). 支持矢量机 (SVM) 和人工神经网络 (ANN) 显示出最有希望的可靠ETc计算.
科学领域:
- 农业气象学 农业气象学
- 水文学的水文学
- 机器学习应用 机器学习应用
背景情况:
- 准确的作物蒸发透气 (ETc) 估计对于水文和农业气象学过程至关重要.
- 现有的ETc数值方法受到参数复杂性,数据可变性和连续性问题的限制.
- 在半干旱地区开发可靠的每日ETc模型对于有效的水资源管理至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于估计玉米作物的每日蒸发转化率 (ETc).
- 为了评估各种ML模型的性能,使用不同的天气输入在半干旱的气候.
- 确定对ETc估计最有影响力的气象参数.
主要方法:
- 开发了五种ML模型:类别提升 (CB),线性回归 (LR),支向量机 (SVM),人工神经网络 (ANN) 和随机梯度下降 (SGD).
- 模型使用来自新德里的ICAR-IARI的数据进行训练和验证.
- 绩效与Penman-Monteith (PM) 模型进行了比较,使用R2,MAE,RMSE和MAPE等指标.
主要成果:
- SVM模型表现出卓越的性能,具有最高的确定系数 (R2 = 0.987) 和最低的误差 (MAE = 0.121 mm day-1,RMSE = 0.172 mm day-1,MAPE = 4.37%).
- 该ANN模型也产生了可比且有前途的结果.
- 风速被确定为影响ETc估计的最重要的输入参数.
结论:
- 支持矢量机 (SVM) 和人工神经网络 (ANN) 模型是准确的每日玉米作物蒸发转化 (ETc) 估计的可靠替代方案.
- 这些ML模型可以在半干旱环境中克服传统数值方法的局限性.
- 这些发现支持在类似的气候条件下改善水资源管理和农业规划.
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