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相关概念视频

Multiple Regression01:25

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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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.

Water science and technology : a journal of the International Association on Water Pollution Research
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PubMed
概括
此摘要是机器生成的。

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.

关键词:
人工神经网络的人工神经网络蒸发透气是指蒸发透气.机器学习是机器学习.随机的森林随机的森林支持矢量机器的支持矢量机器

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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.