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  2. 成都山地灾害与环境研究所
  3. 中国科学院水利部成都山地灾害与环境研究所
  4. 山地灾害与地表过程重点实验室
Artificial Neural Network-based prediction of glacial debris flows in the ParlungZangbo Basin, southeastern Tibetan Plateau, China

文献类型：期刊论文

作者Tang, Wang1; Ding, Hai-tao2; Chen, Ning-sheng2; Ma, Shang-Chang1; Liu, Li-hong2; Wu, Kang-lin2; Tian, Shu-feng2
刊名JOURNAL OF MOUNTAIN SCIENCE
出版日期2021
卷号18期号:1页码:51-67
ISSN号1672-6316
关键词Two layers neural networks Glacial debris flow Disaster events K-fold cross-validation Rainfall Temperature
DOI10.1007/s11629-020-6414-7
通讯作者Ding, Hai-tao(dinghaitao@imde.ac.cn)
产权排序2
文献子类Article
英文摘要Accurate prediction on geological hazards can prevent disaster events in advance and greatly reduce property losses and life casualties. Glacial debris flows are the most serious hazards in southeastern Tibet in China due to their complexity in formation mechanism and the difficulty in prediction. Data collected from 102 glacier debris flow events from 31 gullies since 1970 and regional meteorological data from 1970 to 2019 in ParlungZangbo River Basin in southeastern Tibet were used for Artificial Neural Network (ANN)-based prediction of glacial debris flows. The formation mechanism of glacial debris flows in the ParlungZangbo Basin was systematically analyzed, and the calculations involving the meteorological data and disaster events were conducted by using the statistical methods and two layers fully connected neural networks. The occurrence probabilities and scales of glacial debris flows (small, medium, and large) were predicted, and promising results have been achieved. Through the proposed model calculations, a prediction accuracy of 78.33% was achieved for the scale of glacial debris flows in the study area. The prediction accuracy for both large- and medium-scale debris flows are higher than that for small-scale debris flows. The debris flow scale and the probability of occurrence increase with increasing rainfall and temperature. In addition, the K-fold cross-validation method was used to verify the reliability of the model. The average accuracy of the model calculated under this method is about 93.3%, which validates the proposed model. Practices have proved that the combination of ANN and disaster events can provide sound prediction on geological hazards under complex conditions.
电子版国际标准刊号1993-0321
资助项目National Natural Science Foundation of China[41861134008] ; Sichuan Province Science and Technology Plan Project Key research and development projects[18ZDYF0329]
WOS研究方向Environmental Sciences & Ecology
语种英语
出版者SCIENCE PRESS
WOS记录号WOS:000603539500001
资助机构National Natural Science Foundation of China ; Sichuan Province Science and Technology Plan Project Key research and development projects
源URL[http://ir.imde.ac.cn/handle/131551/55159]  
专题成都山地灾害与环境研究所_山地灾害与地表过程重点实验室
通讯作者Ding, Hai-tao
作者单位1.Chengdu Univ Informat Technol, Chengdu 610225, Peoples R China;
2.Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Hazards & Surface Proc, Chengdu 610041, Peoples R China
推荐引用方式
GB/T 7714		 Tang, Wang,Ding, Hai-tao,Chen, Ning-sheng,et al. Artificial Neural Network-based prediction of glacial debris flows in the ParlungZangbo Basin, southeastern Tibetan Plateau, China[J]. JOURNAL OF MOUNTAIN SCIENCE,2021,18(1):51-67.
APA Tang, Wang.,Ding, Hai-tao.,Chen, Ning-sheng.,Ma, Shang-Chang.,Liu, Li-hong.,...&Tian, Shu-feng.(2021).Artificial Neural Network-based prediction of glacial debris flows in the ParlungZangbo Basin, southeastern Tibetan Plateau, China.JOURNAL OF MOUNTAIN SCIENCE,18(1),51-67.
MLA Tang, Wang,et al."Artificial Neural Network-based prediction of glacial debris flows in the ParlungZangbo Basin, southeastern Tibetan Plateau, China".JOURNAL OF MOUNTAIN SCIENCE 18.1(2021):51-67.

入库方式： OAI收割

来源：成都山地灾害与环境研究所

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