scholarly journals Reservoir Sediment Management Using Artificial Neural Networks: A Case Study of the Lower Section of the Alpine Saalach River

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 818
Author(s):  
Markus Reisenbüchler ◽  
Minh Duc Bui ◽  
Peter Rutschmann
Keyword(s):  
Real World ◽  
Close Correlation ◽  
Critical Issue ◽  
Management Tool ◽  
Lower Section ◽  
Reservoir Sediment ◽  
Efficiency And Effectiveness ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Reservoir sedimentation is a critical issue worldwide, resulting in reduced storage volumes and, thus, reservoir efficiency. Moreover, sedimentation can also increase the flood risk at related facilities. In some cases, drawdown flushing of the reservoir is an appropriate management tool. However, there are various options as to how and when to perform such flushing, which should be optimized in order to maximize its efficiency and effectiveness. This paper proposes an innovative concept, based on an artificial neural network (ANN), to predict the volume of sediment flushed from the reservoir given distinct input parameters. The results obtained from a real-world study area indicate that there is a close correlation between the inputs—including peak discharge and duration of flushing—and the output (i.e., the volume of sediment). The developed ANN can readily be applied at the real-world study site, as a decision-support system for hydropower operators.

scholarly journals (Hyper)graph Kernels over Simplicial Complexes

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1155
Author(s):  
Alessio Martino ◽  
Antonello Rizzi
Keyword(s):  
Pattern Recognition ◽  
Real World ◽  
Simplicial Complexes ◽  
Structural Pattern ◽  
Graph Kernels ◽  
Structural Pattern Recognition ◽  
Learning Tasks ◽  
Efficiency And Effectiveness ◽  
Benchmark Datasets

Graph kernels are one of the mainstream approaches when dealing with measuring similarity between graphs, especially for pattern recognition and machine learning tasks. In turn, graphs gained a lot of attention due to their modeling capabilities for several real-world phenomena ranging from bioinformatics to social network analysis. However, the attention has been recently moved towards hypergraphs, generalization of plain graphs where multi-way relations (other than pairwise relations) can be considered. In this paper, four (hyper)graph kernels are proposed and their efficiency and effectiveness are compared in a twofold fashion. First, by inferring the simplicial complexes on the top of underlying graphs and by performing a comparison among 18 benchmark datasets against state-of-the-art approaches; second, by facing a real-world case study (i.e., metabolic pathways classification) where input data are natively represented by hypergraphs. With this work, we aim at fostering the extension of graph kernels towards hypergraphs and, more in general, bridging the gap between structural pattern recognition and the domain of hypergraphs.

scholarly journals Artificial Neural Network Trained to Predict High-Harmonic Flux

2018 ◽  
Vol 8 (11) ◽  
pp. 2106 ◽  
Author(s):  
Ana Gherman ◽  
Katalin Kovács ◽  
Mircea Cristea ◽  
Valer Toșa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Harmonic ◽  
Ann Model ◽  
Dimensional Parameter ◽  
High Order Harmonic ◽  
Cell Position ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work we present the results obtained with an artificial neural network (ANN) which we trained to predict the expected output of high-order harmonic generation (HHG) process, while exploring a multi-dimensional parameter space. We argue on the utility and efficiency of the ANN model and demonstrate its ability to predict the outcome of HHG simulations. In this case study we present the results for a loose focusing HHG beamline, where the changing parameters are: the laser pulse energy, gas pressure, gas cell position relative to focus and medium length. The physical quantity which we predict here using ANN is directly related to the total harmonic yield in a specified spectral domain (20–40 eV). We discuss the versatility and adaptability of the presented method.

scholarly journals Geomechanical Modelling Using Artificial Neural Networks Combined With Geostatistics

2020 ◽  
Author(s):  
Son Tung Pham
Keyword(s):  
Oil And Gas ◽  
Horizontal Stress ◽  
Kriging Interpolation ◽  
Empirical Formulas ◽  
Stress Estimation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Minimum Horizontal Stress ◽  
Oil And Gas Reservoir

The principal minimum horizontal stress plays an important role in the study of reservoir characteristics, modeling of oil and gas reservoir, drilling, production and stimulating wells. However, it is currently not possible to measure the minimum horizontal stress along the wellbore as a geophysical parameter logging. Minimum horizontal stress is measured by leak-off test (LOT) at only several points in a well. In order to have the values all along the wellbore, experimental formulas were established to determine the minimum horizontal stresses for different fields. Then these formulas must be calibrated with LOT data whose number is usually limited, even sometimes unavailable. On the other hand, the empirical formulas of one field might not be accurate for another. This study presents a new approach to solve the problem of minimum horizontal stress estimation by a combination of artificial intelligence and geostatistics. The method consists of using artificial neural network (ANN) to build a model of minimum horizontal stress estimation from relevant parameters such as true vertical depth, pore pressure and vertical stress, then combined with Kriging interpolation to obtain the distribution in space of the minimum horizontal stress. Hence, this method can estimate the minimum horizontal stress with a limited amount of available data and therefore we do not need to drill new wells or to find empirical formulas for each survey area. The method was then applied in a case study involved real geomechanical dataset of Hai Thach - Moc Tinh field in Nam Con Son basin, Vietnam.

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