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 im- portance 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.
翻译:更深入地了解蒸发的驱动因素及其组成部分(蒸发和蒸发)的建模,对于未来数十年全球水资源的监测和管理具有重大意义。在这项工作中,我们开发了一个框架,从候选一组中找出最佳的机器学习算法,选择最佳的预测特征,以及其远征的分级特征,以便预测准确性。我们的实验将4个湿地地点的3个不同的特征集作为输入8个候选机器学习算法的投入,提供了96套实验配置。鉴于参数数量之多,我们的结果有力地证明,尽管所研究的所有湿地地点有相似之处,但没有在所有这些地点设置任何独特的最佳机器学习算法或特征。在研究特征重要性时发现的一个重要发现是,甲烷通量(其与蒸发热量的关系一般没有研究)可能有助于进一步理解生物物理过程。