Solving transparency in drought forecasting using attention models.

Dikshit, A; Pradhan, B; Assiri, ME; Almazroui, M; Park, H-J

Solving transparency in drought forecasting using attention models.

Dikshit, A

Pradhan, B

Assiri, ME Almazroui, M Park, H-J

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Sci Total Environ, 2022, 837, pp. 155856
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Dikshit, A https://orcid.org/0000-0003-2876-4080
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Assiri, ME
dc.contributor.author	Almazroui, M
dc.contributor.author	Park, H-J
dc.date.accessioned	2023-03-21T02:16:53Z
dc.date.available	2022-05-07
dc.date.available	2023-03-21T02:16:53Z
dc.date.issued	2022-09-01
dc.identifier.citation	Sci Total Environ, 2022, 837, pp. 155856
dc.identifier.issn	0048-9697
dc.identifier.issn	1879-1026
dc.identifier.uri	http://hdl.handle.net/10453/167897
dc.description.abstract	Droughts are one of the most devastating and recurring natural disaster due to a multitude of reasons. Among the different drought studies, drought forecasting is one of the key aspects of effective drought management. The occurrence of droughts is related to a multitude of factors which is a combination of hydro-meteorological and climatic factors. These variables are non-linear in nature, and neural networks have been found to effectively forecast drought. However, classical neural nets often succumb to over-fitting due to various lag components among the variables and therefore, the emergence of new deep learning and explainable models can effectively solve this problem. The present study uses an Attention-based model to forecast meteorological droughts (Standard Precipitation Index) at short-term forecast range (1-3 months) for five sites situated in Eastern Australia. The main aim of the work is to interpret the model outcomes and examine how a deep neural network achieves the forecasting results. The plots show the importance of the variables along with its short-term and long-term dependencies at different lead times. The results indicate the importance of large-scale climatic indices at different sequence dependencies specific to the study site, thus providing an example of the necessity to build a spatio-temporal explainable AI model for drought forecasting. The use of such interpretable models would help the decision-makers and planners to use data-driven models as an effective measure to forecast droughts as they provide transparency and trust while using these models.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	ELSEVIER
dc.relation.ispartof	Sci Total Environ
dc.relation.isbasedon	10.1016/j.scitotenv.2022.155856
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Environmental Sciences
dc.subject.mesh	Australia
dc.subject.mesh	Droughts
dc.subject.mesh	Forecasting
dc.subject.mesh	Meteorology
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Forecasting
dc.subject.mesh	Australia
dc.subject.mesh	Droughts
dc.subject.mesh	Meteorology
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Australia
dc.subject.mesh	Droughts
dc.subject.mesh	Forecasting
dc.subject.mesh	Meteorology
dc.subject.mesh	Neural Networks, Computer
dc.title	Solving transparency in drought forecasting using attention models.
dc.type	Journal Article
utslib.citation.volume	837
utslib.location.activity	Netherlands
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-21T02:16:52Z
pubs.publication-status	Published
pubs.volume	837

Abstract:

Droughts are one of the most devastating and recurring natural disaster due to a multitude of reasons. Among the different drought studies, drought forecasting is one of the key aspects of effective drought management. The occurrence of droughts is related to a multitude of factors which is a combination of hydro-meteorological and climatic factors. These variables are non-linear in nature, and neural networks have been found to effectively forecast drought. However, classical neural nets often succumb to over-fitting due to various lag components among the variables and therefore, the emergence of new deep learning and explainable models can effectively solve this problem. The present study uses an Attention-based model to forecast meteorological droughts (Standard Precipitation Index) at short-term forecast range (1-3 months) for five sites situated in Eastern Australia. The main aim of the work is to interpret the model outcomes and examine how a deep neural network achieves the forecasting results. The plots show the importance of the variables along with its short-term and long-term dependencies at different lead times. The results indicate the importance of large-scale climatic indices at different sequence dependencies specific to the study site, thus providing an example of the necessity to build a spatio-temporal explainable AI model for drought forecasting. The use of such interpretable models would help the decision-makers and planners to use data-driven models as an effective measure to forecast droughts as they provide transparency and trust while using these models.

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http://hdl.handle.net/10453/167897