[M-IS16] Geophysical particulate gravity current

[MIS16-P08] Inverse Analysis of Debris Flow Deposit Using Convolutional Neural Network

*Komoriya Masaya¹, Hajime Naruse¹ (1.Kyoto University Graduate School of Science )

Keywords:Machine Learning, Convolutional Neural Network

This study proposes a new method for estimating the hydraulic conditions of debris flows from the horizontal distribution of their deposit thickness using the convolutional neural network (CNN). Although the forward models for debris flows have been established to reproduce the flow behavior from the initial conditions, it has not been easy to reconstruct the flow conditions from the actual distribution of deposits. In this method, the distributions of deposits are firstly calculated using the forward model with randomly generated initial flow conditions of debris flows. Then, the CNN model explores the relationship between the initial flow conditions and the resulting deposits via training based on this artificial dataset. The CNN model adjusts its internal parameters to estimate the model parameters, including initial flow thickness, radius, friction coefficient, and Coulomb friction angle. The Voellmy fluid model was adopted as the forward model to generate the artificial data sets. The CNN trained with 460 training data sets, and the performance of the trained CNN model was evaluated with 20 test data sets. As a result, inverse analysis on test data sets exhibited that initial flow conditions were precisely reconstructed from depositional properties. Although the accuracy of the estimation varied depending on each model parameter, the forward model reproduced the thickness distribution of debris-flow deposits with sufficient accuracy. Thus, the application of the CNN model to field data sets is anticipated to provide information for assessing the risk of debris flows.