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

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1461 ◽  
Author(s):  
Van Hieu Bui ◽  
Minh Duc Bui ◽  
Peter Rutschmann
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Grain Size ◽  
Discrete Element Method ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Discrete Element ◽  
Fine Sediment ◽  
Gravel Bed ◽  
Artificial Neural

In gravel-bed rivers, monitoring porosity is vital for fluvial geomorphology assessment as well as in r 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.

scholarly journals Correction: Combination of Discrete Element Method and Artificial Neural Network for Predicting Porosity of Gravel-Bed River. Water 2019, 11(7), 1461

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 408
Author(s):  
Van Hieu Bui ◽  
Minh Duc Bui ◽  
Peter Rutschmann
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discrete Element Method ◽  
River Water ◽  
Discrete Element ◽  
Gravel Bed ◽  
Artificial Neural ◽  
Gravel Bed River ◽  
Element Method

We have found one error in Equation (2) (duplicate with Equation (1)) in our paper published in the Water Journal [1]: (1) m i d u → i dt   = m i g → + ∑ k f → i , k + f → i , f [...]

scholarly journals Prediction of size distribution of iron ore granules and permeability of its bed

2011 ◽  
Vol 47 (2) ◽  
pp. 113-123 ◽  
Author(s):  
X. Lv ◽  
C. Bai ◽  
X. Huang ◽  
G. Qiu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Content ◽  
Size Distribution ◽  
Mass Fraction ◽  
Raw Materials ◽  
Good Prediction ◽  
Moisture Capacity ◽  
Granulation Process ◽  
Artificial Neural

The granulation process, which is determined by many factors like properties of the mixture and the operating parameters, is of very importance for getting a good permeability of the burden in the sintering strand. The prediction of the size distribution of the granules and the permeability of its bed by the artificial neural network was studied in this paper. It was found by the experiments that the order of significance in the granulation process is water content added into the mixture, the mass fraction of the particles of 0.7-3 mm, and the moisture capacity. The water content added in the mixture and the mass fractions of the particles of 0.7-3 mm have the positive relation to the permeability of granulation, While, the moisture capacity has the negative relation to the permeability of granulation. Both the moisture capacity and the water content added were used as the inputs in the model of artificial neural network, which can give a good prediction on the permeability and mass fraction of the granules of 3-8 mm, as well as the tendency of the samples under instable raw materials conditions. These two models can be used for optimization the granulation.

scholarly journals Reconstruction of flow conditions from 2004 Indian Ocean tsunami deposits at the Phra Thong island using a deep neural network inverse model

2021 ◽  
Vol 21 (5) ◽  
pp. 1667-1683
Author(s):  
Rimali Mitra ◽  
Hajime Naruse ◽  
Shigehiro Fujino
Keyword(s):  
Neural Network ◽  
Grain Size ◽  
Indian Ocean ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Deep Neural Network ◽  
Inverse Model ◽  
Indian Ocean Tsunami ◽  
Flow Conditions ◽  
2004 Indian Ocean Tsunami

Abstract. The 2004 Indian Ocean tsunami caused significant economic losses and a large number of fatalities in the coastal areas. The estimation of tsunami flow conditions using inverse models has become a fundamental aspect of disaster mitigation and management. Here, a case study involving the Phra Thong island, which was affected by the 2004 Indian Ocean tsunami, in Thailand was conducted using inverse modeling that incorporates a deep neural network (DNN). The DNN inverse analysis reconstructed the values of flow conditions such as maximum inundation distance, flow velocity and maximum flow depth, as well as the sediment concentration of five grain-size classes using the thickness and grain-size distribution of the tsunami deposit from the post-tsunami survey around Phra Thong island. The quantification of uncertainty was also reported using the jackknife method. Using other previous models applied to areas in and around Phra Thong island, the predicted flow conditions were compared with the reported observed values and simulated results. The estimated depositional characteristics such as volume per unit area and grain-size distribution were in line with the measured values from the field survey. These qualitative and quantitative comparisons demonstrated that the DNN inverse model is a potential tool for estimating the physical characteristics of modern tsunamis.

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