Improving prediction and assessment of global fires using multilayer neural networks

Joshi, J and Sukumar, R (2021) Improving prediction and assessment of global fires using multilayer neural networks. In: Scientific Reports, 11 (1).

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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. © 2021, The Author(s).

Item Type:	Journal Article
Publication:	Scientific Reports
Publisher:	Nature Research
Additional Information:	The copyright of this article belongs to Nature Research
Keywords:	Africa; article; artificial neural network; Asia; Australia; carbon sink; cell level; climate change; controlled study; fire; global climate; grid cell; human; human cell; organization; prediction; savanna; scrub; vegetation
Department/Centre:	Division of Biological Sciences > Centre for Ecological Sciences Division of Mechanical Sciences > Divecha Centre for Climate Change
Date Deposited:	15 Mar 2021 11:09
Last Modified:	15 Mar 2021 11:09
URI:	http://eprints.iisc.ac.in/id/eprint/68168

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