Debris flow prediction with machine learning: smart management of urban systems and infrastructures

[1]  B. McArdell Field Measurements of Forces in Debris Flows at the Illgraben: Implications for Channel-Bed Erosion , 2016 .

[2]  Muhammad Sahimi,et al.  Linking Morphology of Porous Media to Their Macroscopic Permeability by Deep Learning , 2020, Transport in Porous Media.

[3]  Y. Bertho,et al.  Stability of a granular layer on an inclined “fakir plane” , 2012, 1212.4659.

[4]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  M. Sahimi,et al.  Physics- and image-based prediction of fluid flow and transport in complex porous membranes and materials by deep learning , 2021 .

[6]  M. Sahimi,et al.  Quantifying accuracy of stochastic methods of reconstructing complex materials by deep learning. , 2020, Physical review. E.

[7]  J. Gray,et al.  Weak, strong and detached oblique shocks in gravity-driven granular free-surface flows , 2007, Journal of Fluid Mechanics.

[8]  Kolumban Hutter,et al.  Gravity-driven free surface flow of granular avalanches over complex basal topography , 1999, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[9]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[10]  C. Ng,et al.  Physical modeling of baffles influence on landslide debris mobility , 2015, Landslides.

[11]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[12]  Eric P. Xing,et al.  GeePS: scalable deep learning on distributed GPUs with a GPU-specialized parameter server , 2016, EuroSys.

[13]  Pejman Tahmasebi,et al.  Hybrid geological modeling: Combining machine learning and multiple-point statistics , 2020, Comput. Geosci..

[14]  James Glover,et al.  INTEGRAL HAZARD MANAGEMENT U SING A UNIFIED SOFTWARE ENVIRONMENT NUMERICAL SIMULATION TOOL "RAMMS" F OR GRAVITATIONAL NATURAL HAZARDS , 2012 .

[15]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[16]  Rolf Katzenbach,et al.  Rapid flow of dry granular materials down inclined chutes impinging on rigid walls , 2007 .

[17]  R. Iverson,et al.  Grain-size segregation and levee formation in geophysical mass flows , 2012 .

[18]  Tao Bai,et al.  Efficient and data-driven prediction of water breakthrough in subsurface systems using deep long short-term memory machine learning , 2020, Computational Geosciences.

[19]  B. Perthame,et al.  A new model of Saint Venant and Savage–Hutter type for gravity driven shallow water flows , 2003 .

[20]  Trevor Darrell,et al.  Fully convolutional networks for semantic segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Chao Yang,et al.  A Survey on Deep Transfer Learning , 2018, ICANN.

[22]  M. Sahimi,et al.  Machine learning in geo- and environmental sciences: From small to large scale , 2020, Advances in Water Resources.

[23]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[24]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[25]  J. Gray,et al.  Gravity-driven granular free-surface flow around a circular cylinder , 2013, Journal of Fluid Mechanics.

[26]  J. Gray,et al.  Multiple solutions for granular flow over a smooth two-dimensional bump , 2017, Journal of Fluid Mechanics.

[27]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[28]  Aronne Armanini,et al.  Two-dimensional simulation of debris flows in erodible channels , 2009, Comput. Geosci..

[29]  Michael Westdickenberg,et al.  Gravity driven shallow water models for arbitrary topography , 2004 .

[30]  I. Einav,et al.  Spread-out and slow-down of granular flows through model forests , 2019, Granular Matter.

[31]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.