Advancing forest fire prediction: A multi-layer stacking ensemble model approach
文献类型：期刊论文
| 作者 | Shahzad, Fahad6; Mehmood, Kaleem4,5; Anees, Shoaib Ahmad3; Adnan, Muhammad2; Muhammad, Sultan4; Haidar, Ijlal5; Ali, Jamshid5; Hussain, Khadim5; Feng, Zhongke6; Khan, Waseem Razzaq1 |
| 刊名 | EARTH SCIENCE INFORMATICS
 |
| 出版日期 | 2025-03-01 |
| 卷号 | 18期号:3页码:270 |
| 关键词 | Forest fire Multi-layer ensemble model Machine learning Probability mapping Malakand division |
| ISSN号 | 1865-0473 |
| DOI | 10.1007/s12145-025-01782-4 |
| 产权排序 | 5 |
| 文献子类 | Article |
| 英文摘要 | A reliable forest fire probability map is vital for disaster management and an essential resource in land use planning. This study evaluates the efficacy of the multi-layer stacking ensemble Machine Learning (ML) method for forest fire susceptibility mapping, presenting a comparative case study within the Malakand division of Pakistan. Our extensive literature review shows that the present ML model has never been used in Pakistan's forest fire scenarios. We employed several benchmark models for comparative evaluation, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN). A comprehensive fire inventory database was constructed, including satellite and ground hotspot data and relevant influencing factors. The fire probability indices from the six models were analyzed and validated using accuracy, area under the curve (AUC), precision, recall, and F1 score evaluation metrics. According to the Performance Evaluation Outcomes, the multi-layer stacking ensemble model provides the best outcomes in terms of accuracy 96.24%, AUC 99.43%, precision 97.81%, recall 94.59%, and F1 96.17% metrics. These results underscore the model's potential as an effective new forest fire Probability mapping tool. Given its evidenced effectiveness, local forestry authorities in the Malakand division should consider its application for enhanced forestry conservation management and fire prevention strategies. |
| URL标识 | 查看原文 |
| WOS关键词 | BURN SEVERITY ; LOGISTIC-REGRESSION ; WILDFIRE OCCURRENCE ; RISK ; AREA ; ALGORITHMS ; PATTERNS ; GIS ; MOUNTAINS ; LANDSCAPE |
| WOS研究方向 | Computer Science ; Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001425944300003 |
| 出版者 | SPRINGER HEIDELBERG |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/212294]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Mehmood, Kaleem; Anees, Shoaib Ahmad |
| 作者单位 | 1.Univ Putra Malaysia UPM, Fac Forestry & Environm, Dept Forestry Sci & Biodivers, Serdang 43400, Selangor, Malaysia 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 3.Univ Agr, Dept Forestry, Dera Ismail Khan 29050, Pakistan; 4.Univ Swat, Inst Forest Sci, Main Campus Charbagh, Swat 19120, Khyber Pakhtunk, Pakistan; 5.Beijing Forestry Univ, Key Lab Silviculture & Conservat, Minist Educ, Beijing 100083, Peoples R China; 6.Beijing Forestry Univ, Mapping & Technol Ctr 3S, Beijing 100083, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Shahzad, Fahad,Mehmood, Kaleem,Anees, Shoaib Ahmad,et al. Advancing forest fire prediction: A multi-layer stacking ensemble model approach[J]. EARTH SCIENCE INFORMATICS,2025,18(3):270. |
| APA | Shahzad, Fahad.,Mehmood, Kaleem.,Anees, Shoaib Ahmad.,Adnan, Muhammad.,Muhammad, Sultan.,...&Khan, Waseem Razzaq.(2025).Advancing forest fire prediction: A multi-layer stacking ensemble model approach.EARTH SCIENCE INFORMATICS,18(3),270. |
| MLA | Shahzad, Fahad,et al."Advancing forest fire prediction: A multi-layer stacking ensemble model approach".EARTH SCIENCE INFORMATICS 18.3(2025):270. |
入库方式： OAI收割
来源：地理科学与资源研究所
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