Vol18 No.1: 51-67
【Title】Artificial Neural Network-based prediction of glacial debris flows in the ParlungZangbo Basin, southeastern Tibetan Plateau, China
【Author】TANG Wang1; DING Hai-tao2*; CHEN Ning-sheng2; MA Shang-Chang1; LIU Li-hong2; WU Kang-lin2; TIAN Shu-feng2
【Addresses】1 Chengdu University of Information Technology, Chengdu 610225, China; 2 Key Laboratory of Mountain Hazards and Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
【Corresponding author】DING Hai-tao
【Citation】Tang W, Ding HT, Chen NS, et al. (2021) Artificial Neural network-based prediction of glacial debris flows in the ParlungZangbo Basin, southeastern Tibetan Plateau, China. Journal of Mountain Science 18(1). https://doi.org/10.1007/s11629-020-6414-7
【Abstract】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 Chinadue 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 wassystematically 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 increasewith 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.
【Keywords】Two layers neural networks; Glacial debris flow; Disaster events; K-fold cross-validation; Rainfall; Temperature