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 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-o

SUBMITTER: Joshi J 

PROVIDER: S-EPMC7870964 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature