ABSTRACT Forecasting of irrigation demand is important for decision-making, and reference evapotranspiration (ETo) is a key determinant in evaluating water demand in advance. However, the precise determination of ETo is fairly difficult, and complex machine learning approaches are often used for this. This study, carried out in Veeranam tank, India, determines the multivariate analysis of correlated variables involved in the estimation and modelling of ETo from 1995 to 2016. A reduced-feature data model was constructed with the most significant variables of the model extracted by principal component analysis. This work also explores the effectiveness of a deep learning neural network (DLNN) with the reduced-feature model in predicting ETo in comparison with the conventional Food and Agriculture Organization of the United Nations (FAO-56) Penman-Monteith equation and the radial basis function neural network (RBFNN) as a baseline machine learning method. The input variable dimensionality was reduced from six to three most significant variables in ETo modelling. Among machine learning methods, DLNN proved to be effective in ETo prediction with the reduced-feature data model.

中文翻译：

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使用机器学习预测灌溉调度的参考蒸发量

摘要 灌溉需求的预测对于决策很重要，参考蒸散量 (ETo) 是提前评估需水量的关键决定因素。然而，ETo 的精确确定相当困难，为此经常使用复杂的机器学习方法。本研究在印度 Veeranam tank 进行，确定了 1995 年至 2016 年 ETo 估计和建模中涉及的相关变量的多变量分析。成分分析。与传统的联合国粮食及农业组织 (FAO-56) Penman-Monteith 方程和径向基相比，这项工作还探讨了具有缩减特征模型的深度学习神经网络 (DLNN) 在预测 ETo 方面的有效性函数神经网络（RBFNN）作为基线机器学习方法。在 ETo 建模中，输入变量维度从六个最重要的变量减少到三个。在机器学习方法中，DLNN 被证明在使用减少特征数据​​模型的 ETo 预测中是有效的。