数字农科院2.0

Daily-Scale Fire Risk Assessment for Eastern Mongolian Grasslands by Integrating Multi-Source Remote Sensing and Machine Learning

文献类型： 外文期刊

作者： Risu Na;Byambakhuu Gantumur;Wala Du;Sainbuyan Bayarsaikhan;Yu Shan;Qier Mu;Yuhai Bao;Nyamaa Tegshjargal;Battsengel Vandansambuu

作者机构：

关键词： grassland fire in eastern Mongolia;hyperparameter optimization;machine learning (RF, XGBoost, DNN);risk prediction;SHAP interpretability

期刊名称： Fire

ISSN： 2571-6255

年卷期： 2025 年 8 卷 7 期

页码：

收录情况： SCIE（2025版）

摘要： Frequent wildfires in the eastern grasslands of Mongolia pose significant threats to the ecological environment and pastoral livelihoods, creating an urgent need for high-temporal-resolution and high-precision fire prediction. To address this, this study established a daily-scale grassland fire risk assessment framework integrating multi-source remote sensing data to enhance predictive capabilities in eastern Mongolia. Utilizing fire point data from eastern Mongolia (2012–2022), we fused multiple feature variables and developed and optimized three models: random forest (RF), XGBoost, and deep neural network (DNN). Model performance was enhanced using Bayesian hyperparameter optimization via Optuna. Results indicate that the Bayesian-optimized XGBoost model achieved the best generalization performance, with an overall accuracy of 92.3%. Shapley additive explanations (SHAP) interpretability analysis revealed that daily-scale meteorological factors—daily average relative humidity, daily average wind speed, daily maximum temperature—and the normalized difference vegetation index (NDVI) were consistently among the top four contributing variables across all three models, identifying them as key drivers of fire occurrence. Spatiotemporal validation using historical fire data from 2023 demonstrated that fire points recorded on 8 April and 1 May 2023 fell within areas predicted to have “extremely high” fire risk probability on those respective days. Moreover, points A (117.36° E, 46.70° N) and B (116.34° E, 49.57° N) exhibited the highest number of days classified as “high” or “extremely high” risk during the April/May and September/October periods, consistent with actual fire occurrences. In summary, the integration of multi-source data fusion and Bayesian-optimized machine learning has enabled the first high-precision daily-scale wildfire risk prediction for the eastern Mongolian grasslands, thus providing a scientific foundation and decision-making support for wildfire prevention and control in the region.

分类号：

  • 相关文献

[1]OVERSAMPLING METHOD FOR ENVIRONMENTAL MONITORING DATA AUGMENTATION IN CADMIUM -POLLUTED PADDY FIELD. Hao, Peitong,Li, Yue,Xu, Weiman,Li, Cong,Huo, Mingzhu,Zhang, Xiaoyu,An, Yi. 2022

[2]The Application Mode of Multi-Dimensional Time Series Data Based on a Multi-Stage Neural Network. Ting Wang,Na Wang,Yunpeng Cui,Juan Liu. 2023

作者其他论文 更多>>