使用机器学习方法估计参考蒸发透气
Bhavya T R1, Ananta Vashisth2, P Krishnan1
1ICAR-Indian Agricultural Research Institute, Pusa, New Delhi 110 012, India.
概括
机器学习模型使用有限的天气数据准确估计蒸发透气 (ET0). 随机森林,支持向量机和人工神经网络模型显示了在小麦种植地区改善ET0预测的潜力.
科学领域:
- 农业气象 农业气象学
- 人工智能在水文学中的应用
背景情况:
- 蒸发转化 (ET0) 对于作物水管理至关重要.
- 精确的ET0估计依赖于关键的天气参数,如温度,太阳辐射,湿度和风速.
研究的目的:
- 开发和比较机器学习模型,以改善在小麦生长期间的ET0估计.
- 为了确定最佳的天气输入组合,以准确地预测ET0.
主要方法:
- 收集了印度阿姆里萨尔的每日天气数据 (1970-2018).
- 开发了随机森林 (RF),支持矢量机 (SVM) 和人工神经网络 (ANN) 模型.
- 评估模型使用统计标准进行校准和验证.
主要成果:
- 射频模型表现出最佳性能,其次是SVM和ANN.
- 使用Tmax,Tmin,RHM,RHE和Rs的模型获得了最高的排名.
- 特定的输入组合 (Rs,Tmax; Rs,Tmin; Tmax,Tmin) 显示了与RF,SVM和ANN分别的出色性能.
结论:
- 机器学习技术可以用更少的数据点准确地估计ET0.
- 当数据有限时,选择的输入组合适用于ET0估计.
- 这种方法在数据稀缺的情况下提高了ET0估计的实用性.
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