Improving prediction and assessment of global fires using multilayer neural networks

Abstract Fires determine vegetation patterns, impact human societies, and are a part of complex feedbacks into the global climate system. Empirical and process-based models differ in their scale and mechanistic assumptions, giving divergent predictions of fire drivers and extent. Although humans hav...

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Autores principales:	Jaideep Joshi, Raman Sukumar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a3ab8a19941149dc9df7cb901805c0e6
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spelling	oai:doaj.org-article:a3ab8a19941149dc9df7cb901805c0e62021-12-02T13:30:22ZImproving prediction and assessment of global fires using multilayer neural networks10.1038/s41598-021-81233-42045-2322https://doaj.org/article/a3ab8a19941149dc9df7cb901805c0e62021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81233-4https://doaj.org/toc/2045-2322Abstract Fires determine vegetation patterns, impact human societies, and are a part of complex feedbacks into the global climate system. Empirical and process-based models differ in their scale and mechanistic assumptions, giving divergent predictions of fire drivers and extent. Although humans have historically used and managed fires, the current role of anthropogenic drivers of fires remains less quantified. Whereas patterns in fire–climate interactions are consistent across the globe, fire–human–vegetation relationships vary strongly by region. Taking a data-driven approach, we use an artificial neural network to learn region-specific relationships between fire and its socio-environmental drivers across the globe. As a result, our models achieve higher predictability as compared to many state-of-the-art fire models, with global spatial correlation of 0.92, monthly temporal correlation of 0.76, interannual correlation of 0.69, and grid-cell level correlation of 0.60, between predicted and observed burned area. Given the current socio-anthropogenic conditions, Equatorial Asia, southern Africa, and Australia show a strong sensitivity of burned area to temperature whereas northern Africa shows a strong negative sensitivity. Overall, forests and shrublands show a stronger sensitivity of burned area to temperature compared to savannas, potentially weakening their status as carbon sinks under future climate-change scenarios.Jaideep JoshiRaman SukumarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jaideep Joshi Raman Sukumar Improving prediction and assessment of global fires using multilayer neural networks
description	Abstract Fires determine vegetation patterns, impact human societies, and are a part of complex feedbacks into the global climate system. Empirical and process-based models differ in their scale and mechanistic assumptions, giving divergent predictions of fire drivers and extent. Although humans have historically used and managed fires, the current role of anthropogenic drivers of fires remains less quantified. Whereas patterns in fire–climate interactions are consistent across the globe, fire–human–vegetation relationships vary strongly by region. Taking a data-driven approach, we use an artificial neural network to learn region-specific relationships between fire and its socio-environmental drivers across the globe. As a result, our models achieve higher predictability as compared to many state-of-the-art fire models, with global spatial correlation of 0.92, monthly temporal correlation of 0.76, interannual correlation of 0.69, and grid-cell level correlation of 0.60, between predicted and observed burned area. Given the current socio-anthropogenic conditions, Equatorial Asia, southern Africa, and Australia show a strong sensitivity of burned area to temperature whereas northern Africa shows a strong negative sensitivity. Overall, forests and shrublands show a stronger sensitivity of burned area to temperature compared to savannas, potentially weakening their status as carbon sinks under future climate-change scenarios.
format	article
author	Jaideep Joshi Raman Sukumar
author_facet	Jaideep Joshi Raman Sukumar
author_sort	Jaideep Joshi
title	Improving prediction and assessment of global fires using multilayer neural networks
title_short	Improving prediction and assessment of global fires using multilayer neural networks
title_full	Improving prediction and assessment of global fires using multilayer neural networks
title_fullStr	Improving prediction and assessment of global fires using multilayer neural networks
title_full_unstemmed	Improving prediction and assessment of global fires using multilayer neural networks
title_sort	improving prediction and assessment of global fires using multilayer neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a3ab8a19941149dc9df7cb901805c0e6
work_keys_str_mv	AT jaideepjoshi improvingpredictionandassessmentofglobalfiresusingmultilayerneuralnetworks AT ramansukumar improvingpredictionandassessmentofglobalfiresusingmultilayerneuralnetworks
_version_	1718392931453239296

Improving prediction and assessment of global fires using multilayer neural networks

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