arXiv Open Access 2022

AutoML-Based Drought Forecast with Meteorological Variables

Shiheng Duan Xiurui Zhang

Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

cs.LG physics.ao-ph

Penulis (2)

Shiheng Duan

Xiurui Zhang

Format Sitasi

APA MLA BibTeX

Duan, S., Zhang, X. (2022). AutoML-Based Drought Forecast with Meteorological Variables. https://arxiv.org/abs/2207.07012

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓