Visualization of Prediction Methods for Wildfire Modeling Using CiteSpace: A Bibliometric Analysis

Pan, Minhu; Zhang, Shuo

doi:10.3390/atmos14061009

Atmosphere

2023

DOI: 10.3390/atmos14061009

|View full text |Cite

Visualization of Prediction Methods for Wildfire Modeling Using CiteSpace: A Bibliometric Analysis

Minhu Pan

Shuo Zhang

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” a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Research hotspots and evolution trends of food safety risk assessment techniques and methods

Wang,

2024

eFood

View full text Add to dashboard Cite

Food safety incidents frequently occur in China and other countries. Food safety risk management has evolved from harm‐based to risk‐based approaches to control risks more scientifically and effectively. Risk assessment is at the core of risk control and provides a scientific basis for the development of risk management measures. Therefore, analyzing the research status and development trends of food safety risk assessment technologies and methods is important for improving food safety risk assessment and risk management. In this study, CiteSpace software was used to analyze a knowledge graph of popular research topics in the field of food safety risk assessment over the past 14 years, identify research keywords, and provide centrality statistics. Food safety risk assessments primarily focus on in vitro high‐throughput testing, computational toxicology, predictive microbiology, mechanistic research, and other technical methods. In the future, international coordination, cooperation, and cross‐integration of relevant disciplines should be strengthened, and research on and application of innovative technologies for food safety risk assessment should be promoted. Research on food safety risk assessment technologies and methods is in an active development stage and has good prospects for further exploration and application.

show abstract

Research hotspots and evolution trends of food safety risk assessment techniques and methods

Wang,

2024

eFood

View full text Add to dashboard Cite

show abstract

Integrated Anomaly Detection and Early Warning System for Forest Fires in the Odisha Region

Hiremath,

Kannan

2024

Atmosphere

View full text Add to dashboard Cite

The present study aims to develop a random forest algorithm-based classifier to predict the occurrence of fire events using observed meteorological parameters a day in advance. We considered the skin temperature, the air temperature close to the surface, the humidity close to the surface level, and soil moisture as important meteorological factors influencing forest fire occurrence. Twenty additional parameters were derived based on these four parameters that account for the energy exchanged in sensible and latent forms and the change in parameters in recent trends. We used the mutual information approach to identify critical meteorological parameters that carry significant information about fire occurrence the next day. The top nine parameters were then fed as input to the random forest algorithm to predict fire/no fire the next day. The weighted data sampling and SMOTE techniques were employed to address the class imbalance in the fire data class. Both techniques correctly classified fire incidents well, given the meteorological input from the previous days. This study also showed that as the class imbalance increases to 1:9, the performance based on the precision, recall, F1 score, and accuracy are maximum, showing the model’s ability to perform with class imbalance. Both techniques helped the random forest algorithm forecast fire instances as the data sample size increased.

show abstract

An AutoML-Powered Analysis Framework for Forest Fire Forecasting: Adapting to Climate Change Dynamics

Zhang,

Pan

2024

Atmosphere

View full text Add to dashboard Cite

Wildfires pose a serious threat to ecosystems and human safety, and with the backdrop of global climate change, the prediction of forest fires has become increasingly important. Traditional machine learning methods face challenges in forest fire prediction, such as difficulty identifying feature parameters, manual intervention in model selection, and hyperparameter tuning, which affect prediction accuracy and efficiency. This study proposes an analytical framework for forest fire prediction based on Automated Machine Learning (AutoML) technology to address the challenges traditional machine learning methods face in forest fire prediction. We collected meteorological, topographical, and vegetation data from Guangxi Province, with meteorological data covering 1994 to 2023, providing comprehensive background information for our prediction model. Using the prediction model, which was constructed with the AutoGluon framework, the experimental results indicate that models under the AutoGluon framework (e.g., KNeighborsDist classifier) significantly outperform traditional machine learning models in terms of accuracy, precision, recall, and F1-Score, with the highest accuracy rate reaching 0.960. Model error analysis shows that models under the AutoGluon framework perform better in error control. This study provides an efficient and accurate method for forest fire prediction, which is of great significance for decision-making in forest fire management and for protecting forest resources and ecological security.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Visualization of Prediction Methods for Wildfire Modeling Using CiteSpace: A Bibliometric Analysis

Cited by 5 publications

References 23 publications

Research hotspots and evolution trends of food safety risk assessment techniques and methods

Research hotspots and evolution trends of food safety risk assessment techniques and methods

Integrated Anomaly Detection and Early Warning System for Forest Fires in the Odisha Region

An AutoML-Powered Analysis Framework for Forest Fire Forecasting: Adapting to Climate Change Dynamics

Contact Info

Product

Resources

About