Mapping wildfire ignition probability and predictor sensitivity with ensemble-based machine learning

Qi Tong and Thomas Gernay ()
Additional contact information
Qi Tong: Johns Hopkins University
Thomas Gernay: Johns Hopkins University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 119, issue 3, No 17, 1582 pages

Abstract: Abstract Wildfire ignition models can help in identifying risk factors and mapping high-risk areas, which addresses an urgent issue as wildfires become increasingly destructive. Despite advancements in data collection and data analysis, challenges persist as ignitions depend on numerous interconnected factors at a fine spatial resolution. Predicting wildfire ignitions from data is an imbalanced classification problem, given the vast number of non-ignition data compared to ignition data. To address this issue, this study proposes an ensemble-based model for binary classification of wildfire ignitions. The data are collected for a 24,867 km2 area in northern California from January 2014 to May 2022 and includes 76 predictors covering topographic, land cover, anthropogenic, and climatic data. Different base classifiers are evaluated and the random forest is found the most performant, yielding a recall of 0.67 and a specificity of 0.87. Feature importance analysis shows that the Topographic Wetness Index is the most important climatic predictor, while population density and land cover development are also highly rated. Comparison of yearly average of computed daily probabilities with ignition data shows that the model accurately captures the spatial pattern of ignitions, which can reveal high-risk areas. The model is then used to assess how climatic and anthropogenic factors impact wildfire ignition frequency. The projected scenarios show that the number and spread of ignitions would significantly increase with an increase in population in sparsely populated areas, while climatic factors have secondary effects in isolation but in combination may compound the risk. As current land development and climate change trends are expected to increase the frequency and severity of wildfires, data-based models can provide insights to inform policy and mitigate risk.

Keywords: Wildfires; Ignitions; California; Ensemble-based model; Machine learning (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-023-06172-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:119:y:2023:i:3:d:10.1007_s11069-023-06172-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-023-06172-x

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().