论文标题
用于构建机器学习模型的框架，该模型具有功能集优化用于蒸发分区的框架
A Framework for Constructing Machine Learning Models with Feature Set Optimisation for Evapotranspiration Partitioning
论文作者
论文摘要
对蒸散剂的驱动器及其组成部分的建模（蒸发和蒸腾）的更深入了解对于未来几十年来全球水资源的监测和管理可能至关重要。在这项工作中，我们开发了一个框架，以确定候选人集的最佳性能机器学习算法，选择最佳预测功能以及对预测精度的重要性的排名。我们的实验在4个湿地站点上使用了3个单独的功能集作为对8种候选机器学习算法的输入，提供了96组实验配置。鉴于这一参数数量大量，我们的结果表明，有充分的证据表明，尽管它们相似，但在所有研究的湿地网站上都没有奇异的最佳机器学习算法或特征。研究特征重要性时发现的一个关键发现是，通常不研究其与蒸散液的关系的甲烷通量可能有助于进一步的生物物理过程理解。
A deeper understanding of the drivers of evapotranspiration and the modelling of its constituent parts (evaporation and transpiration) could be of significant importance to the monitoring and management of water resources globally over the coming decades. In this work, we developed a framework to identify the best performing machine learning algorithm from a candidate set, select optimal predictive features as well as ranking features in terms of their importance to predictive accuracy. Our experiments used 3 separate feature sets across 4 wetland sites as input into 8 candidate machine learning algorithms, providing 96 sets of experimental configurations. Given this high number of parameters, our results show strong evidence that there is no singularly optimal machine learning algorithm or feature set across all of the wetland sites studied despite their similarities. A key finding discovered when examining feature importance is that methane flux, a feature whose relationship with evapotranspiration is not generally examined, may contribute to further biophysical process understanding.
