DroughtED: A dataset and methodology for drought forecasting spanning multiple climate zones

Christoph Minixhofer, Mark Swan, Calum McMeekin, Pavlos Andreadis

Research output: Contribution to conferencePaperpeer-review


Climate change exacerbates the frequency, duration and extent of extreme weather events such as drought. Previous attempts to forecast drought conditions using machine learning have focused on regional models which have two major limitations for national drought management: (i) they are trained on localised climate data and (ii) their architectures prevent them from being applied to new heterogeneous regions. In this work, we present a new large-scale dataset for training machine learning models to forecast national drought conditions, named DroughtED. The dataset consists of globally available meteorological features widely used for drought prediction, paired with location meta-data which has not previously been utilised for drought forecasting. Here we also establish a baseline on DroughtED and present the first research to apply deep learning models - Long Short-Term Memory (LSTMs) and Transformers - to predict county-level drought conditions across the full extent of the United States. Our results indicate that DroughtED enables deep learning models to learn cross-region patterns in climate data that contribute to drought conditions and models trained on DroughtED compare favourably to state-of-the-art drought prediction models trained on individual regions.
Original languageEnglish
Number of pages8
Publication statusPublished - 23 Jul 2021
EventTackling Climate Change with Machine Learning: Workshop at ICML 2021 - Virtual
Duration: 23 Jul 202123 Jul 2021


WorkshopTackling Climate Change with Machine Learning
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