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...

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Autores principales:	E. Felsche, R. Ludwig
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350 Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/08f07a6ac58d472cbee517d11574ce75
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spelling	oai:doaj.org-article:08f07a6ac58d472cbee517d11574ce752021-12-03T12:25:24ZApplying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations10.5194/nhess-21-3679-20211561-86331684-9981https://doaj.org/article/08f07a6ac58d472cbee517d11574ce752021-12-01T00:00:00Zhttps://nhess.copernicus.org/articles/21/3679/2021/nhess-21-3679-2021.pdfhttps://doaj.org/toc/1561-8633https://doaj.org/toc/1684-9981<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>E. FelscheE. FelscheR. LudwigCopernicus PublicationsarticleEnvironmental technology. Sanitary engineeringTD1-1066Geography. Anthropology. RecreationGEnvironmental sciencesGE1-350GeologyQE1-996.5ENNatural Hazards and Earth System Sciences, Vol 21, Pp 3679-3691 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350 Geology QE1-996.5
spellingShingle	Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350 Geology QE1-996.5 E. Felsche E. Felsche R. Ludwig Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations
description	<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>
format	article
author	E. Felsche E. Felsche R. Ludwig
author_facet	E. Felsche E. Felsche R. Ludwig
author_sort	E. Felsche
title	Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations
title_short	Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations
title_full	Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations
title_fullStr	Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations
title_full_unstemmed	Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations
title_sort	applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/08f07a6ac58d472cbee517d11574ce75
work_keys_str_mv	AT efelsche applyingmachinelearningfordroughtpredictioninaperfectmodelframeworkusingdatafromalargeensembleofclimatesimulations AT efelsche applyingmachinelearningfordroughtpredictioninaperfectmodelframeworkusingdatafromalargeensembleofclimatesimulations AT rludwig applyingmachinelearningfordroughtpredictioninaperfectmodelframeworkusingdatafromalargeensembleofclimatesimulations
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Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations

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