论文标题
基于气象变量的基于汽车的干旱预测
AutoML-Based Drought Forecast with Meteorological Variables
论文作者
论文摘要
对干旱的确切预测对于科学研究，农业和水资源管理具有相当大的价值。随着用于水力气候建模的数据驱动方法的新兴发展，本文研究了一种基于汽车的框架，以预测美国的干旱与常用的时间深度学习模型相比，自动摩托车模型可以通过较少的培训数据和时间来实现可比性的性能。随着深度学习模型在地球系统建模中变得流行，本文旨在为基于汽车的方法带来更多努力，并将其用作更复杂的深度学习模型的基准基准。
A precise forecast for droughts is of considerable value to scientific research, agriculture, and water resource management. With emerging developments of data-driven approaches for hydro-climate modeling, this paper investigates an AutoML-based framework to forecast droughts in the U.S. Compared with commonly-used temporal deep learning models, the AutoML model can achieve comparable performance with less training data and time. As deep learning models are becoming popular for Earth system modeling, this paper aims to bring more efforts to AutoML-based methods, and the use of them as benchmark baselines for more complex deep learning models.
