Artificial intelligence and spatial Modelling in natural hazards and environmental applications

Pradhan, B

Artificial intelligence and spatial Modelling in natural hazards and environmental applications

Pradhan, B

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Publisher:: SPRINGER INTERNATIONAL PUBLISHING AG
Publication Type:: Chapter
Citation:: Advances in Science, Technology and Innovation, 2019, pp. 7-10
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.editor	ElAskary, HM
dc.contributor.editor	Lee, S
dc.contributor.editor	Heggy, E
dc.contributor.editor	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2023-01-03T22:15:19Z
dc.date.available	2023-01-03T22:15:19Z
dc.date.issued	2019-01-01
dc.identifier.citation	Advances in Science, Technology and Innovation, 2019, pp. 7-10
dc.identifier.isbn	978-3-030-01439-1
dc.identifier.uri	http://hdl.handle.net/10453/164653
dc.description.abstract	Modeling and predicting geohazards is extremely difficult due to their complex behavior in the real-world. In fact, several aspects of these environmental applications are considered in computer-based modeling to accurately estimating real-world phenomena. Till date, none of the proposed methods have reached to zero uncertainties or errors to recognize the entire disaster’s events. Globally, many people have lost their lives due to various types of natural hazards. Therefore, it is important to detect, monitor and predict them to protect the inhabitants against the potential natural hazards that threaten human lives and properties. Recently, artificial intelligent (AI) methods have received a great deal of attraction due to their precision to model the complex problems such as natural hazards. AI can see different aspects of a complex problem with sufficient iteration and details. In recent years, implementation of AI models coupled with geospatial information systems (GIS) are the most efficient and accurate approach to model natural disasters i.e. flooding, earthquake, landslides, forest fire and drought rather than other existing methods. This gives an insight into the ability of applied AI models in some natural hazards applications.
dc.language	en
dc.publisher	SPRINGER INTERNATIONAL PUBLISHING AG
dc.relation.ispartof	Advances in Science, Technology and Innovation
dc.relation.ispartofseries	Advances in Science Technology & Innovation
dc.relation.isbasedon	10.1007/978-3-030-01440-7_3
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Artificial intelligence and spatial Modelling in natural hazards and environmental applications
dc.type	Chapter
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	2023-01-03T22:15:18Z
pubs.publication-status	Published

Abstract:

Modeling and predicting geohazards is extremely difficult due to their complex behavior in the real-world. In fact, several aspects of these environmental applications are considered in computer-based modeling to accurately estimating real-world phenomena. Till date, none of the proposed methods have reached to zero uncertainties or errors to recognize the entire disaster’s events. Globally, many people have lost their lives due to various types of natural hazards. Therefore, it is important to detect, monitor and predict them to protect the inhabitants against the potential natural hazards that threaten human lives and properties. Recently, artificial intelligent (AI) methods have received a great deal of attraction due to their precision to model the complex problems such as natural hazards. AI can see different aspects of a complex problem with sufficient iteration and details. In recent years, implementation of AI models coupled with geospatial information systems (GIS) are the most efficient and accurate approach to model natural disasters i.e. flooding, earthquake, landslides, forest fire and drought rather than other existing methods. This gives an insight into the ability of applied AI models in some natural hazards applications.

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http://hdl.handle.net/10453/164653