ORCID Profile
0000-0003-1775-5445
Current Organisation
Boise State University
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Publisher: Oxford University Press (OUP)
Date: 07-2022
DOI: 10.1093/PNASNEXUS/PGAC115
Abstract: Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts (ii) embrace different ways of knowing and knowledge generation (iii) promote exploration of fundamental science (iv) capitalize on the “firehose” of data for societal benefit and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across erse communities can we effectively undertake research that improves outcomes in our more fiery future.
Publisher: Elsevier BV
Date: 11-2020
Publisher: Elsevier BV
Date: 07-2023
Publisher: Elsevier BV
Date: 12-2021
Publisher: Elsevier BV
Date: 2023
Publisher: Wiley
Date: 06-2022
DOI: 10.1002/HYP.14596
Abstract: Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process‐based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in EES, (1) nowcasting and prediction, (2) scenario analysis, and (3) diagnostic learning. The paper ponders over a ‘coevolutionary’ approach to model building, shifting away from a borrowing to a co‐creation culture, to develop a generation of models that leverage the unique strengths of ML such as scalability to big data and high‐dimensional mapping, while remaining faithful to process‐based knowledge base and principles of model explainability and interpretability, and therefore, falsifiability.
Publisher: Elsevier BV
Date: 04-2020
No related grants have been discovered for Mojtaba Sadegh.