Forecasting daily potential evapotranspiration using machine learning and limited climatic data
Forecasting daily potential evapotranspiration using machine learning and limited climatic data
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DOI：
10.1016/j.agwat.2010.10.012
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发表时间：
2011-02
期刊：
影响因子：
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通讯作者：
A. Torres;W. Walker;M. McKee
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文献类型：
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作者：
A. Torres;W. Walker;M. McKee
预测或预报近期灌溉需求是改善运输和输送系统管理的一项要求。灌溉预测制度的最重要组成部分是一个简单，但可靠的，预测作物需水量的方法，在本文中是由参考或潜在蒸散量（ETO）。在大多数情况下，该地区的天气数据仅限于测量的变量数量减少，因此当前或未来的ETO估计受到限制。本文总结了在上述条件下对两种ETo预报方案的检验结果。第一种或“直接”方法涉及预测ETout历史计算的ETo值。第二种或“间接”方法是根据历史数据预测计算ETo所需的天气参数，然后计算ETo。一种统计机器学习算法，多变量相关向量机（MVRVM）被应用到这两个预测方案。一般使用的ETModel是1985年哈格里夫斯方程，它只需要最低和最高日气温，因此非常适合缺乏更全面的气候数据的地区。预测方法的实用性和实用性与应用程序在中央犹他州的灌溉项目证明。为了确定所应用的算法的优点和适用性，另一个学习机，多层感知器（MLP），用于比较的目的。通过自举分析测试了所提方案的鲁棒性和稳定性。
Anticipating, or forecasting near-term irrigation demands is a requirement for improved management of conveyance and delivery systems. The most important component of a forecasting regime for irrigation is a simple, yet reliable, approach for forecasting crop water demands, which in this paper is represented by the reference or potential evapotranspiration (ETo). In most cases, weather data in the area is limited to a reduced number of variables measured, therefore current or future EToestimation is restricted. This paper summarizes the results of testing of two proposed forecasting EToschemes under the mentioned conditions. The first or “direct” approach involved forecasting ETousing historically computed ETovalues. The second or “indirect” approach involved forecasting the required weather parameters for the ETocalculation based on historical data and then computing ETo. An statistical machine learning algorithm, the Multivariate Relevance Vector Machine (MVRVM) is applied to both of the forecastings schemes. The general ETomodel used is the 1985 Hargreaves Equation which requires only minimum and maximum daily air temperatures and is thus well suited to regions lacking more comprehensive climatic data. The utility and practicality of the forecasting methodology is demonstrated with an application to an irrigation project in Central Utah. To determine the advantage and suitability of the applied algorithm, another learning machine, the Multilayer Perceptron (MLP), is used for comparison purposes. The robustness and stability of the proposed schemes are tested by the application of the bootstrap analysis.