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Combination of Discrete Element Method and Artificial Neural Network for Predicting Porosity of Gravel-Bed River

1
Institute of Hydraulic and Water Resources Engineering, Technische Universität München, Arcisstrasse 21, D-80333 München, Germany
2
Faculty of Mechanical Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi 100000, Vietnam
*
Author to whom correspondence should be addressed.
Water 2019, 11(7), 1461; https://doi.org/10.3390/w11071461
Received: 12 June 2019 / Revised: 5 July 2019 / Accepted: 12 July 2019 / Published: 14 July 2019
(This article belongs to the Section Hydraulics)
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Abstract

In gravel-bed rivers, monitoring porosity is vital for fluvial geomorphology assessment as well as in river ecosystem management. Conventional porosity prediction methods are restricting in terms of the number of considered factors and are also time-consuming. We present a framework, the combination of the Discrete Element Method (DEM) and Artificial Neural Network (ANN), to study the relationship between porosity and the grain size distribution. DEM was applied to simulate the 3D structure of the packing gravel-bed and fine sediment infiltration processes under various forces. The results of the DEM simulations were verified with the experimental data of porosity and fine sediment distribution. Further, an algorithm was developed for calculating high-resolution results of porosity and grain size distribution in vertical and horizontal directions from the DEM results, which were applied to develop a Feed Forward Neural Network (FNN) to predict bed porosity based on grain size distribution. The reliable results of DEM simulation and FNN prediction confirm that our framework is successful in predicting porosity change of gravel-bed. View Full-Text
Keywords: mathematical modelling; DEM; ANN; bed porosity; grain sorting; gravel-bed river mathematical modelling; DEM; ANN; bed porosity; grain sorting; gravel-bed river
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Bui, V.H.; Bui, M.D.; Rutschmann, P. Combination of Discrete Element Method and Artificial Neural Network for Predicting Porosity of Gravel-Bed River. Water 2019, 11, 1461.

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