Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations
<p>There is a strong scientific and social interest in understanding the factors leading to extreme events in order to improve the management of risks associated with hazards like droughts. In this study, artificial neural networks are applied to predict the occurrence of a drought in two cont...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2021-12-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://nhess.copernicus.org/articles/21/3679/2021/nhess-21-3679-2021.pdf |
| Summary: | <p>There is a strong scientific and social interest in understanding the factors
leading to extreme events in order to improve the management of risks
associated with hazards like droughts. In this study, artificial neural
networks are applied to predict the occurrence of a drought in two contrasting
European domains, Munich and Lisbon, with a lead time of 1 month. The
approach takes into account a list of 28 atmospheric and soil variables as
input parameters from a single-model initial-condition large ensemble
(CRCM5-LE). The data were produced in the context of the ClimEx project by
Ouranos, with the Canadian Regional Climate Model (CRCM5) driven by 50 members
of the Canadian Earth System Model (CanESM2). Drought occurrence is defined
using the standardized precipitation index. The best-performing machine
learning algorithms manage to obtain a correct classification of drought or no
drought for a lead time of 1 month for around 55 %–57 % of the
events of each class for both domains. Explainable AI methods like SHapley
Additive exPlanations (SHAP) are applied to understand the trained algorithms
better. Variables like the North Atlantic Oscillation index and air pressure
1 month before the event prove essential for the prediction. The study shows
that seasonality strongly influences the performance of drought prediction,
especially for the Lisbon domain.</p> |
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| ISSN: | 1561-8633 1684-9981 |