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.

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.

Forest-fire is a pressing global problem that has far-reaching effects on human life and the environment, with climate change exacerbating their frequency and intensity. There is an urgent need for advanced predictive systems to mitigate these impacts. To address this issue, this study introduces a forest-fire prediction framework integrating wireless sensor networks (WSNs), data analysis, and machine learning. Sensor nodes deployed in a forest area collected real-time meteorological data, which was transmitted using LoRaWAN technology. Data mining techniques prepared the data for analysis using the SVM algorithm, revealing relationships between meteorological parameters and wildfire risk. The SVM model demonstrated an accuracy of 86% in classifying forest-fire risk levels based on temperature, humidity, wind speed, and rainfall data. The integrated framework of WSNs and the SVM algorithm provides a high-accuracy model for forest-fire risk prediction. The model is compared to the Canadian Forest Fire Hazard Rating System to validate its accuracy, demonstrating strong agreement with historical records and reports. The model's practical implications include efficient management, early detection, and prevention strategies. However, the model's limitations suggest avenues for future research, we should consider broader geographic applications and using advanced machine-learning methods to enhance the model's predictive capabilities.