AutoML-Based Drought Forecast with Meteorological Variables
This work addresses drought prediction for agriculture and water management, but it is incremental as it applies an existing AutoML method to a specific domain.
The paper tackled drought forecasting in the U.S. using an AutoML-based framework, achieving comparable performance to temporal deep learning models with less training data and time.
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.