Research Trends in Wildland Fire Prediction Amidst Climate Change: A Comprehensive Bibliometric Analysis
Abstract
:1. Introduction
2. Data and Methods
2.1. Data Source
2.2. Methods
3. Results and Discussion
3.1. Analysis of Publication Outputs
3.2. Analysis of Core Authors
3.3. High-Yielding Journals
3.4. High-Output Countries and Institutes
3.5. Co-Citation Analysis of Papers
3.6. Analysis of Terms
3.7. Analysis of the Keywords Cluster
3.8. Trend Analysis
3.8.1. Machine Learning
3.8.2. Wildland Fire Spread Model Prediction
3.8.3. Climate Change and Environmental Changes Caused by Wildland Fires
4. Conclusions
- This systematic literature review provides reliable results and understanding. The analysis demonstrates that research in the field of wildfire prediction is growing, with particular increases from 2019 to 2023. The majority of research institutions are from the United States Forest Service and the Chinese Academy of Sciences, with publications primarily distributed in forestry and remote sensing journals. The top-cited journals are the International Journal of Wildland Fire, Forest Ecology and Management, Remote Sensing, and Forests. The top 10 countries in terms of publication count are from Europe, with the United States and China being the top two. According to the high-frequency authors’ research content, the main research topics in forest fire prediction include applying models to predict fire occurrence probability, predicting and simulating fire spread, predicting environmental changes after fire, and the impact of climate change on fire prediction. The most cited paper is Jain P’s 2020 review article on machine learning in forest fire prediction, which will have a significant influence on the development of this field in the future. Future research can consider more fire-driven factors and compare different algorithm models to achieve higher accuracy.
- The literature analysis reveals that the hotspots of forest fire prediction research include machine learning applications in forest fire prediction, the impact of climate change on fire prediction, fire spread simulation, and environmental impact prediction. Since 2020, there has been a significant increase in the application of machine learning in forest fire prediction. Although machine learning has shown high accuracy, other algorithms, such as deep learning and ensemble learning, have also demonstrated strong performance in wildfire prediction. The accuracy of most models used is closely related to the selection of fire-driven factors.
- According to multiple data, deep learning and ensemble learning have higher accuracy rates in forest fire prediction than single machine learning algorithm models, indicating the future research trend in this field.
- Fire spread simulation and prediction are based on simulating and predicting fire behavior after a fire has occurred, and cellular automata have high applicability in simulating fire behavior. However, the analysis suggests that combining machine learning with cellular automata will significantly improve simulation efficiency and accuracy.
- Climate change factors are crucial considerations in wildfire prediction work, not only due to internal changes in the forest but also external meteorological changes that have a significant impact on the forest environment. Accurately predicting fire occurrence under the influence of climate change is a future research trend.
- Although fire prediction is primarily focused on fire behavior and fire occurrence probability, predicting the environmental impact and ecological damage caused by fire is also a requirement for forest fire managers. Incorporating predictions of environmental impact and ecological damage into wildfire prediction models could provide valuable insights for wildfire managers and support more comprehensive wildfire management strategies.
Author Contributions
Funding
Conflicts of Interest
References
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Publications | Author | H Index | Research Hotspots and Content |
---|---|---|---|
32 | Viegas, D.X. | 30 | Simulation of Wildland fire Spread and Canopy Fire Dynamics |
23 | Margalef, T. | 14 | Wildland fire Spread Prediction |
23 | Cortes, A. | 16 | Optimization of Wildland fire Spread Models and Development of a Fire Spread Simulator |
20 | Sullivan, A.L. | 26 | Influence of Fuel and Slope on Wildland fire Spread” |
19 | Cruz, M.G. | 29 | Developing a Wildfire Spread Model and Examining the Influence of Fuel Factors |
19 | Robichaud, P.R. | 38 | Predicting and Assessing Environmental Changes Following Wildfire Events |
17 | Flannigan, M.D. | 72 | The Role of Climate Change Factors in Wildfire Occurrence |
17 | Alexander, M.E. | 31 | Improving Fire Spread Models and Predicting Crown Fire Behavior |
16 | Penman, T.D. | 34 | The Impact of Changes in Wildfire Drivers on Prediction Accuracy |
16 | Guo, F. | 16 | Machine Learning for Predicting Wildfire Susceptibility |
Rank | Country | Publications | Centrality | Continent |
---|---|---|---|---|
1 | USA | 928 | 0.27 | North America |
2 | China | 354 | 0.24 | Asia |
3 | Australia | 315 | 0.20 | Oceania |
4 | Canada | 262 | 0.06 | North America |
5 | Spain | 211 | 0.09 | Europe |
6 | England | 112 | 0.10 | Europe |
7 | Portugal | 96 | 0.07 | Europe |
8 | India | 90 | 0.07 | Asia |
9 | Germany | 86 | 0.08 | Europe |
10 | France | 84 | 0.13 | Europe |
Rank | Institution | Publications | Centrality | Country |
---|---|---|---|---|
1 | US Forest Service | 205 | 0.30 | USA |
2 | Chinese Academy of Sciences | 64 | 0.11 | China |
3 | University of Melbourne | 52 | 0.07 | Australia |
4 | Natural Resources Canada | 48 | 0.06 | Canada |
5 | Colorado State University | 45 | 0.06 | USA |
6 | Northeast Forestry University | 42 | 0.01 | China |
7 | University of California, Berkeley | 36 | 0.09 | USA |
8 | United States Geological Survey | 34 | 0.05 | USA |
9 | University of Alberta | 34 | 0.08 | Canada |
10 | University of Washington | 33 | 0.03 | USA |
Title | Author | Journal | Year | Citation Frequency |
---|---|---|---|---|
A review of machine learning applications in wildfire science and management | Jain, P. | Environmental Reviews | 2020 | 65 |
Wildland fire susceptibility modeling using a convolutional neural network for the Yunnan Province of China | Zhang, G.L. | International Journal of Disaster Risk Science | 2019 | 54 |
A hybrid artificial intelligence approach using a GIS-based neural-fuzzy inference system and particle swarm optimization for wildland fire susceptibility modeling in a tropical area | Bui, D. | Agricultural and Forest Meteorology | 2017 | 52 |
Impact of anthropogenic climate change on wildfire across western US forests | Abatzoglou, J.T. | PNAS | 2016 | 47 |
Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability | Jaafari, A. | Agricultural and Forest Meteorology | 2019 | 40 |
Climate-induced variations in global wildfire danger from 1979 to 2013 | Jolly, W.M. | Nature Communications | 2015 | 39 |
Investigation of general indicators influencing on wildland fire and its susceptibility modeling using different data mining techniques | Pourtaghi, Z.S. | Ecological Indicators | 2015 | 37 |
GIS-based evolutionary optimized gradient-boosted decision trees for wildland fire susceptibility mapping | Sachdeva, S. | Natural Hazards | 2018 | 33 |
A novel ensemble modeling approach for the spatial prediction of tropical wildland fire susceptibility using a LogitBoost machine learning classifier and multi-source geospatial data | Tehrany, M.S. | Theoretical and Applied Climatology | 2019 | 32 |
Applying genetic algorithms to set the optimal combination of wildland fire related variables and model wildland fire susceptibility based on data mining models. The case of Dayu County, China | Hong, H.Y. | Science of The Total Environment | 2018 | 31 |
Number | Frequency | Centrality | Keyword | Number | Frequency | Centrality | Keyword |
---|---|---|---|---|---|---|---|
1 | 412 | 0.04 | model | 11 | 179 | 0.05 | management |
2 | 389 | 0.03 | prediction | 12 | 168 | 0.03 | impact |
3 | 363 | 0.05 | wildland fire | 13 | 157 | 0.02 | wildland fire |
4 | 349 | 0.06 | forest | 14 | 154 | 0.03 | climate |
5 | 333 | 0.06 | climate change | 15 | 144 | 0.02 | risk |
6 | 288 | 0.07 | fire | 16 | 138 | 0.02 | spread |
7 | 254 | 0.04 | wildfire | 17 | 122 | 0.04 | system |
8 | 223 | 0.06 | vegetation | 18 | 120 | 0.03 | behavior |
9 | 185 | 0.03 | dynamics | 19 | 119 | 0.01 | machine learning |
10 | 184 | 0.05 | pattern | 20 | 115 | 0.03 | logistic regression |
Cluster Number | Cluster Name | Cluster Size | Average Citation Year | Top 1–5 Keywords in Each Cluster |
---|---|---|---|---|
#0 | Fire | 103 | 2009 | climate change; vegetation; regeneration; resilience; soil burn severity |
#1 | Machine learning | 96 | 2015 | machine learning; deep learning; bushfire management; unmanned aerial vehicle; dynamic brightness |
#2 | Fire behavior | 89 | 2012 | wildland fire; fire spread; wildland fire; building standards; physics-based simulation |
#3 | Air quality | 70 | 2010 | climate change; prescribed burning; forest management; soil erodibility; support vector machines |
#4 | Logistic regression | 68 | 2009 | wildland fire; relevance vector machines; imperialist competitive algorithm; sensitivity analysis; wildfire spread |
#5 | Fuel load | 58 | 2016 | machine learning; wildland fire; ensemble models; Bayesian optimization; hyperparameter tuning |
#6 | Soil erosion | 49 | 2015 | soil erosion; forest management; gauged–ungauged watersheds; decision-support tools; sediment yield |
#7 | Fire management | 39 | 2012 | fire management; pyrogenic carbon; organic matter; carbon sequestration; risk assessment |
#8 | Multiple regression | 28 | 2017 | terrestrial lidar; surface fuel load; airborne lidar; multiple regression; litter-bed fuel depth |
#9 | Water storage | 23 | 2013 | bark thickness; crown damage; flame-front residence time; fireline intensity; cambium damage |
#10 | Burn probability | 18 | 2007 | assemblage; model; habitat fragmentation; mortality; edge |
#11 | Wildland fire propagation | 3 | 2016 | wildland fire propagation; wind field; domain decomposition; Schur method |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Bao, M.; Liu, J.; Ren, H.; Liu, S.; Ren, C.; Chen, C.; Liu, J. Research Trends in Wildland Fire Prediction Amidst Climate Change: A Comprehensive Bibliometric Analysis. Forests 2024, 15, 1197. https://doi.org/10.3390/f15071197
Bao M, Liu J, Ren H, Liu S, Ren C, Chen C, Liu J. Research Trends in Wildland Fire Prediction Amidst Climate Change: A Comprehensive Bibliometric Analysis. Forests. 2024; 15(7):1197. https://doi.org/10.3390/f15071197
Chicago/Turabian StyleBao, Mingwei, Jiahao Liu, Hong Ren, Suting Liu, Caixia Ren, Chen Chen, and Jianxiang Liu. 2024. "Research Trends in Wildland Fire Prediction Amidst Climate Change: A Comprehensive Bibliometric Analysis" Forests 15, no. 7: 1197. https://doi.org/10.3390/f15071197
APA StyleBao, M., Liu, J., Ren, H., Liu, S., Ren, C., Chen, C., & Liu, J. (2024). Research Trends in Wildland Fire Prediction Amidst Climate Change: A Comprehensive Bibliometric Analysis. Forests, 15(7), 1197. https://doi.org/10.3390/f15071197