Assessment of debris flow hazards using a Bayesian Network
文献类型：SCI/SSCI论文
| 作者 | Liang W. J.; Zhuang D. F. ; Jiang D.; Pan J. J.; Ren H. Y.
|
| 发表日期 | 2012 |
| 关键词 | Debris Flow Hazard Bayesian Network Hazard Assessment Chinese Mainland Shallow Landslide Susceptibility Artificial Neural-networks Hong-kong Natural Slopes Lantau Island Water-quality Gis Prediction Mountains Inventory |
| 英文摘要 | Comprehensive assessment of debris flow hazard risk is challenging due to the complexity and uncertainties of various related factors. A reasonable and reliable assessment should be based on sufficient data and realistic approaches. This study presents a novel appeoach for assessing debris flow hazard risk using BN (Bayesian Network) and domain knowledge. Based on the records of debris flow hazards and geomorphological/environmental data for the Chinese mainland, approaches based on BN, SVM (Support Vector Machine) and ANN (Artificial Neural Network) were compared. BN provided the highest values of hazard detection probability, precision, and AUC (area under the receiver operating characteristic curve). The BN model is useful for mapping and assessing debris flow hazard risk on a national scale. (C) 2012 Elsevier B.V. All rights reserved. |
| 出处 | Geomorphology |
| 卷 | 171 |
| 页 | 94-100 |
| 语种 | 英语 |
| ISSN号 | 0169-555X |
| 源URL | [http://192.168.22.105/handle/311030/30824]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 中国科学院地理科学与资源研究所 |
| 推荐引用方式 GB/T 7714 | Liang W. J.,Zhuang D. F.,Jiang D.,et al. Assessment of debris flow hazards using a Bayesian Network. 2012. |
入库方式： OAI收割
来源：地理科学与资源研究所
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