Pathways and challenges of the application of artificial intelligence to geohazards modelling

Dikshit, A; Pradhan, B; Alamri, AM

Pathways and challenges of the application of artificial intelligence to geohazards modelling

Dikshit, A

Pradhan, B

Alamri, AM

Permalink

Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Gondwana Research, 2021
Issue Date:: 2021

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted ManuscriptAdobe PDF (1.71 MB)

View on publisher's site

View statistics

Full metadata record

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	Alamri, AM
dc.date.accessioned	2021-05-05T01:03:05Z
dc.date.available	2021-05-05T01:03:05Z
dc.date.issued	2021
dc.identifier.citation	Gondwana Research, 2021
dc.identifier.issn	1342-937X
dc.identifier.uri	http://hdl.handle.net/10453/148660
dc.description.abstract	© 2020 International Association for Gondwana Research The application of artificial intelligence (AI) and machine learning in geohazard modelling has been rapidly growing in recent years, a trend that is observed in several research and application areas thanks to recent advances in AI. As a result, the increasing dependence on data driven studies has made its practical applications towards geohazards (landslides, debris flows, earthquakes, droughts, floods, glacier studies) an interesting prospect. These aforementioned geohazards were responsible for roughly 80% of the economic loss in the past two decades caused by all natural hazards. The present study analyses the various domains of geohazards which have benefited from classical machine learning approaches and highlights the future course of direction in this field. The emergence of deep learning has fulfilled several gaps in: i) classification; ii) seasonal forecasting as well as forecasting at longer lead times; iii) temporal based change detection. Apart from the usual challenges of dataset availability, climate change and anthropogenic activities, this review paper emphasizes that the future studies should focus on consecutive events along with integration of physical models. The recent catastrophe in Japan and Australia makes a compelling argument to focus towards consecutive events. The availability of higher temporal resolution and multi-hazard dataset will prove to be essential, but the key would be to integrate it with physical models which would improve our understanding of the mechanism involved both in single and consecutive hazard scenario. Geohazards would eventually be a data problem, like geosciences, and therefore it is essential to develop models that would be capable of handling large voluminous data. The future works should also revolve towards interpretable models with the hope of providing a reasonable explanation of the results, thereby achieving the ultimate goal of Explainable AI.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Gondwana Research
dc.relation.isbasedon	10.1016/j.gr.2020.08.007
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0402 Geochemistry, 0403 Geology, 0404 Geophysics
dc.subject.classification	Geology
dc.title	Pathways and challenges of the application of artificial intelligence to geohazards modelling
dc.type	Journal Article
utslib.for	0402 Geochemistry
utslib.for	0403 Geology
utslib.for	0404 Geophysics
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/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-05T01:03:03Z
pubs.publication-status	Published

Abstract:

© 2020 International Association for Gondwana Research The application of artificial intelligence (AI) and machine learning in geohazard modelling has been rapidly growing in recent years, a trend that is observed in several research and application areas thanks to recent advances in AI. As a result, the increasing dependence on data driven studies has made its practical applications towards geohazards (landslides, debris flows, earthquakes, droughts, floods, glacier studies) an interesting prospect. These aforementioned geohazards were responsible for roughly 80% of the economic loss in the past two decades caused by all natural hazards. The present study analyses the various domains of geohazards which have benefited from classical machine learning approaches and highlights the future course of direction in this field. The emergence of deep learning has fulfilled several gaps in: i) classification; ii) seasonal forecasting as well as forecasting at longer lead times; iii) temporal based change detection. Apart from the usual challenges of dataset availability, climate change and anthropogenic activities, this review paper emphasizes that the future studies should focus on consecutive events along with integration of physical models. The recent catastrophe in Japan and Australia makes a compelling argument to focus towards consecutive events. The availability of higher temporal resolution and multi-hazard dataset will prove to be essential, but the key would be to integrate it with physical models which would improve our understanding of the mechanism involved both in single and consecutive hazard scenario. Geohazards would eventually be a data problem, like geosciences, and therefore it is essential to develop models that would be capable of handling large voluminous data. The future works should also revolve towards interpretable models with the hope of providing a reasonable explanation of the results, thereby achieving the ultimate goal of Explainable AI.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/148660