Intelligent spatial interpolation-based frost prediction methodology using artificial neural networks with limited local data

Zhou, I; Lipman, J; Abolhasan, M; Shariati, N

Intelligent spatial interpolation-based frost prediction methodology using artificial neural networks with limited local data

Zhou, I

Lipman, J

Abolhasan, M

Shariati, N

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Environmental Modelling and Software, 2023, 165
Issue Date:: 2023-07-01

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Field	Value	Language
dc.contributor.author	Zhou, I https://orcid.org/0000-0002-7154-4561
dc.contributor.author	Lipman, J https://orcid.org/0000-0003-2877-1168
dc.contributor.author	Abolhasan, M https://orcid.org/0000-0002-4282-6666
dc.contributor.author	Shariati, N
dc.date.accessioned	2024-02-21T10:58:35Z
dc.date.available	2024-02-21T10:58:35Z
dc.date.issued	2023-07-01
dc.identifier.citation	Environmental Modelling and Software, 2023, 165
dc.identifier.issn	1364-8152
dc.identifier.issn	1873-6726
dc.identifier.uri	http://hdl.handle.net/10453/175813
dc.description.abstract	The weather phenomenon of frost poses great threats to agriculture. As recent frost prediction methods are based on on-site historical data and sensors, extra development and deployment time are required for data collection in any new site. The aim of this article is to eliminate the dependency on on-site historical data and sensors for frost prediction methods. In this article, a frost prediction method based on spatial interpolation is proposed. The models use climate data from existing weather stations, digital elevation models surveys, and normalized difference vegetation index data to estimate a target site's next hour minimum temperature. The proposed method utilizes ensemble learning to increase the model accuracy. Climate datasets are obtained from 75 weather stations across New South Wales and Australian Capital Territory areas of Australia. The results show that the proposed method reached a detection rate up to 92.55%.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation	Food Agility CRC Limited
dc.relation.ispartof	Environmental Modelling and Software
dc.relation.isbasedon	10.1016/j.envsoft.2023.105724
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Environmental Engineering
dc.title	Intelligent spatial interpolation-based frost prediction methodology using artificial neural networks with limited local data
dc.type	Journal Article
utslib.citation.volume	165
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 - CRIN - Realtime Information Networks
pubs.organisational-group	University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-07-01T00:00:00+1000Z
dc.date.updated	2024-02-21T10:58:31Z
pubs.publication-status	Published
pubs.volume	165

Abstract:

The weather phenomenon of frost poses great threats to agriculture. As recent frost prediction methods are based on on-site historical data and sensors, extra development and deployment time are required for data collection in any new site. The aim of this article is to eliminate the dependency on on-site historical data and sensors for frost prediction methods. In this article, a frost prediction method based on spatial interpolation is proposed. The models use climate data from existing weather stations, digital elevation models surveys, and normalized difference vegetation index data to estimate a target site's next hour minimum temperature. The proposed method utilizes ensemble learning to increase the model accuracy. Climate datasets are obtained from 75 weather stations across New South Wales and Australian Capital Territory areas of Australia. The results show that the proposed method reached a detection rate up to 92.55%.

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

http://hdl.handle.net/10453/175813