scholarly journals A comparative evaluation of shear stress modeling based on machine learning methods in small streams

2015 ◽  
Vol 17 (5) ◽  
pp. 805-816 ◽  
Author(s):  
Onur Genç ◽  
Bilal Gonen ◽  
Mehmet Ardıçlıoğlu
Keyword(s):  
Machine Learning ◽  
Shear Stress ◽  
Stress Distribution ◽  
Measurement Data ◽  
Machine Learning Algorithms ◽  
Shear Stress Distribution ◽  
Classification And Regression Tree ◽  
Data Set ◽  
Small Streams ◽  
Artificial Neural

Predicting shear stress distribution has proved to be a critical problem to solve. Hence, the basic objective of this paper is to develop a prediction of shear stress distribution by machine learning algorithms including artificial neural networks, classification and regression tree, generalized linear models. The data set, which is large and feature-rich, is utilized to improve machine learning-based predictive models and extract the most important predictive factors. The 10-fold cross-validation approach was used to determine the performances of prediction methods. The predictive performances of the proposed models were found to be very close to each other. However, the results indicated that the artificial neural network, which has the R value of 0.92 ± 0.03, achieved the best classification performance overall accuracy on the 10-fold holdout sample. The predictions of all machine learning models were well correlated with measurement data.

scholarly journals Investigation of Shear Stress Distribution in a 90 Degree Channel Bend

2019 ◽  
Vol 24 (1) ◽  
pp. 213-220
Author(s):  
Seung Kyu Lee ◽  
Truong An Dang ◽  
Van Tuan Le
Keyword(s):  
Shear Stress ◽  
Stress Distribution ◽  
River Flow ◽  
Morphological Changes ◽  
Measurement Data ◽  
Shear Stress Distribution ◽  
Doppler Velocity ◽  
Channel Bend ◽  
Outer Bank ◽  
Maximum Value

Abstract Shear stress is a key parameter that plays an important role in sediment transport mechanisms; therefore, understanding shear stress distribution in rivers, and especially in river bends, is necessary to predict erosion, deposition mechanisms and lateral channel migration. The aim of this study is to analyze the shear stress distribution near a river bed at 90-degree channel bend using a depth-average method based on experimental measurement data. Bed shear stress distribution is calculated using the depth-averaged method based on velocity components data has been collected from a 3D-ADV device (three-dimensional acoustic doppler velocity) at different locations of a meandering channel. Laboratory experiments have been made at the hydraulic laboratory of the RCRFIDF (Research Center for River Flow Impingement and Debris Flow), Gangneung-Wonju National University, South Korea to provide data for simulating the incipient motion of the riverbed materials and then predicting the river morphological changes in the curved rivers. The calculated results show that the maximum value of shear stress distribution near the riverbed in the different cross sections of the surveyed channel occurs in a 70-degree cross section and occurs near the outer bank. From the beginning of a 40-degree curved channel section, the maximum value of the shear stress occurs near the outer bank at the end of the channel.

scholarly journals Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models

2020 ◽  
Vol 14 (5) ◽  
pp. 1097-1109
Author(s):  
Zohreh Sheikh Khozani ◽  
Khabat Khosravi ◽  
Mohammadamin Torabi ◽  
Amir Mosavi ◽  
Bahram Rezaei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Shear Stress ◽  
Stress Distribution ◽  
Shear Stress Distribution ◽  
Learning Models ◽  
Compound Channels ◽  
Using Data ◽  
Machine Learning Models

scholarly journals Automatic Lithofacies Classification with t-SNE and K-Nearest Neighbors Algorithm

2021 ◽  
Vol 44 ◽  
Author(s):  
Guilherme Loriato Potratz ◽  
Smith Washington Arauco Canchumuni ◽  
Jose David Bermudez Castro ◽  
Júlia Potratz ◽  
Marco Aurélio C. Pacheco
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Unsupervised Classification ◽  
Machine Learning Algorithms ◽  
Hydrocarbon Exploration ◽  
Data Set ◽  
Log Data ◽  
Hidden Patterns ◽  
Artificial Neural

One of the critical processes in the exploration of hydrocarbons is the identification and prediction of lithofacies that constitute the reservoir. One of the cheapest and most efficient ways to carry out that process is from the interpretation of well log data, which are often obtained continuously and in the majority of drilled wells. The main methodologies used to correlate log data to data obtained in well cores are based on statistical analyses, machine learning models and artificial neural networks. This study aims to test an algorithm of dimension reduction of data together with an unsupervised classification method of predicting lithofacies automatically. The performance of the methodology presented was compared to predictions made with artificial neural networks. We used the t-Distributed Stochastic Neighbor Embedding (t-SNE) as an algorithm for mapping the wells logging data in a smaller feature space. Then, the predictions of facies are performed using a KNN algorithm. The method is assessed in the public dataset of the Hugoton and Panoma fields. Prediction of facies through traditional artificial neural networks obtained an accuracy of 69%, where facies predicted through the t-SNE + K-NN algorithm obtained an accuracy of 79%. Considering the nature of the data, which have high dimensionality and are not linearly correlated, the efficiency of t SNE+KNN can be explained by the ability of the algorithm to identify hidden patterns in a fuzzy boundary in data set. It is important to stress that the application of machine learning algorithms offers relevant benefits to the hydrocarbon exploration sector, such as identifying hidden patterns in high-dimensional datasets, searching for complex and non-linear relationships, and avoiding the need for a preliminary definition of mathematic relations among the model’s input data.

scholarly journals A Bayesian network-based data analytical approach to predict velocity distribution in small streams

2015 ◽  
Vol 18 (3) ◽  
pp. 466-480 ◽  
Author(s):  
Onur Genc ◽  
Ali Dag
Keyword(s):  
Machine Learning ◽  
Bayesian Network ◽  
Risk Score ◽  
Analytical Approach ◽  
Machine Learning Algorithms ◽  
Important Goal ◽  
Data Set ◽  
Small Streams ◽  
Dimensional Parameters ◽  
Probabilistic Risk

Developing a reliable data analytical method for predicting the velocity profile in small streams is important in that it substantially decreases the amount of money and effort spent on measurement procedures. In recent studies it has been shown that machine learning models can be used to achieve such an important goal. In the proposed framework, a tree-augmented Naïve Bayes approach, a member of the Bayesian network family, is employed to address the aforementioned two issues. Therefore, the proposed study presents novelty in that it explores the relations among the predictor attributes and derives a probabilistic risk score associated with the predictions. The data set of four key stations, in two different basins, are employed and the eight observational variables and calculated non-dimensional parameters were utilized as inputs to the models for estimating the response values, u (point velocities in measured verticals). The results showed that the proposed data-analytical approach yields comparable results when compared to the widely used, powerful machine learning algorithms. More importantly, novel information is gained through exploring the interrelations among the predictors as well as deriving a case-specific probabilistic risk score for the prediction accuracy. These findings can be utilized to help field engineers to improve their decision-making mechanism in small streams.

scholarly journals Teaching a neural network modeling socio-economic development of the region

2019 ◽  
Vol 2 (2) ◽  
pp. 34-40
Author(s):  
S. V. Romanchukov ◽  
O. G. Berestneva ◽  
L. A. Petrova
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Information Technologies ◽  
Research Team ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Third Party ◽  
Neural Network Modeling ◽  
Data Set ◽  
Artificial Neural

The article is devoted to the formation of an array of data for the construction of an artificial neural network, designed to search for relationships between social and economic parameters of the development of regions of the Russian Federation. The relevance of research in this area is confirmed both by a large number of studies in the field of regional comparativistics and by the limited methods used in this kind of research, often limited to descriptive methods and basic techniques of parametric statistics. Under these conditions, the expansion of the mathematical apparatus and the more active introduction of information technologies (including in the area of Big Data analysis and the construction of predictive models based on artificial neural networks) can be viable. At the same time, however, it should be noted that the resources of an individual research team may be (and most likely will be) insufficient to create their own software solution for the implementation of machine learning algorithms from scratch. The use of third-party cloud-based software platforms (primarily IBM and Google infrastructures) allows to bypass the problem of the research team’s lack of expensive material and technical base, however they impose a number of limitations dictated by the requirements of the existing machine learning algorithms and the specific architecture provided platforms This puts the research team in front of the need to prepare the accumulated data set for processing: reducing the dimension, checking the data for compliance with the platform requirements and eliminating potential problem areas: “data leaks”, “learning distortions” and others. The paper was reported to the section “Sociology of Digital Society: Structures, Processes, Governance” of the International Conference Session “Public Administration and Development of Russia: National Goals and Institutions”.

scholarly journals Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 596
Author(s):  
Babak Lashkar-Ara ◽  
Niloofar Kalantari ◽  
Zohreh Sheikh Khozani ◽  
Amir Mosavi
Keyword(s):  
Shear Stress ◽  
Stress Distribution ◽  
Fuzzy Inference ◽  
Rectangular Channel ◽  
Tsallis Entropy ◽  
Shear Stress Distribution ◽  
Data Series ◽  
Shear Stresses ◽  
Inference System ◽  
Aspect Ratios

One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel. To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations.

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

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