Affiliation:
1. School of Information Management, Nanjing University, Nanjing 210023, China
Abstract
Wildfire is a growing concern worldwide with significant impacts on human lives and the environment. This study aimed to provide an overview of the current trends and research gaps in wildfire prediction by conducting a bibliometric analysis of papers in the Web of Science and Scopus databases. CiteSpace was employed to analyze the co-occurrence of keywords, identify clusters, and detect emerging trends. The results showed that the most frequently occurring keywords were “wildfire”, “prediction”, and “model” and the top three clusters were related to “air quality”, “history”, and “validation”. The analysis of emerging trends revealed a focus on vegetation, precipitation, land use, trends, and the random forest algorithm. The study contributes to a better understanding of the research trends and gaps in wildfire prediction and provides recommendations for future research, such as incorporating new data sources and using advanced techniques.
Subject
Atmospheric Science,Environmental Science (miscellaneous)
Reference24 articles.
1. A decision tree algorithm for wildfire prediction based on wireless sensor networks;Gao;Int. J. Embed. Syst.,2020
2. Zhang, S., Gao, D., Lin, H., and Sun, Q. (2019). Wildfire detection using sound spectrum analysis based on the internet of things. Sensors, 19.
3. Impact of climate change on biodiversity and associated key ecosystem services in Africa: A systematic review;Sintayehu;Ecosyst. Health Sustain.,2018
4. Climate risk perception and media framing;Brito;RAUSP Manag. J.,2020
5. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability;Jaafari;Agric. For. Meteorol.,2019