scholarly journals Prediction of erosional rates for cohesive sediments in annular flume experiments using artificial neural networks

2018 ◽  
Vol 1 (2) ◽  
pp. 99-111
Author(s):  
Olya Skulovich ◽  
Caroline Ganal ◽  
Leonie K. Nüßer ◽  
Catrina Cofalla ◽  
Holger Schuettrumpf ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Training Sample ◽  
River Rhine ◽  
Chemical Parameters ◽  
Flume Experiments ◽  
Annular Flume ◽  
Physical Chemical ◽  
Artificial Neural

Abstract Artificial neural network is used to predict development of suspended sediment concentration in annular flume experiments on cohesive sediment erosion. Natural sediment for the experiments was taken from the River Rhine and subjected to a consecutive increase in the bed shear stress. The development of the suspended particulate matter (SPM) was measured and then utilized for artificial neural network training, validation, and testing, including independent testing on new data sets. Several network configurations were examined, in particular, with and without autoregressive input. Additionally, relative importance of auxiliary physical-chemical parameters was analyzed. Artificial neural network with autoregressive input showed very high precision in the SPM prediction for all independent test cases achieving average mean squared error 0.034 and regression value 0.998. It was found that for an abundant training sample, the SPM parameter itself is enough to obtain high quality prediction. At the same time, physical-chemical parameters may provide some improvement to the artificial neural network prediction in cases that comprise values unprecedented in the training sample.

Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network 

2021 ◽  
Author(s):  
Sascha Flaig ◽  
Timothy Praditia ◽  
Alexander Kissinger ◽  
Ulrich Lang ◽  
Sergey Oladyshkin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Meteorological Data ◽  
Water Levels ◽  
Typical Structure ◽  
Water Abstraction ◽  
Physical Knowledge ◽  
Artificial Neural ◽  
The Impact

<p>In order to prevent possible negative impacts of water abstraction in an ecologically sensitive moor south of Munich (Germany), a “predictive control” scheme is in place. We design an artificial neural network (ANN) to provide predictions of moor water levels and to separate hydrological from anthropogenic effects. As the moor is a dynamic system, we adopt the „Long short-term memory“ architecture.</p><p>To find the best LSTM setup, we train, test and compare LSTMs with two different structures: (1) the non-recurrent one-to-one structure, where the series of inputs are accumulated and fed into the LSTM; and (2) the recurrent many-to-many structure, where inputs gradually enter the LSTM (including LSTM forecasts from previous forecast time steps). The outputs of our LSTMs then feed into a readout layer that converts the hidden states into water level predictions. We hypothesize that the recurrent structure is the better structure because it better resembles the typical structure of differential equations for dynamic systems, as they would usually be used for hydro(geo)logical systems. We evaluate the comparison with the mean squared error as test metric, and conclude that the recurrent many-to-many LSTM performs better for the analyzed complex situations. It also produces plausible predictions with reasonable accuracy for seven days prediction horizon.</p><p>Furthermore, we analyze the impact of preprocessing meteorological data to evapotranspiration data using typical ETA models. Inserting knowledge into the LSTM in the form of ETA models (rather than implicitly having the LSTM learn the ETA relations) leads to superior prediction results. This finding aligns well with current ideas on physically-inspired machine learning.</p><p>As an additional validation step, we investigate whether our ANN is able to correctly identify both anthropogenic and natural influences and their interaction. To this end, we investigate two comparable pumping events under different meteorological conditions. Results indicate that all individual and combined influences of input parameters on water levels can be represented well. The neural networks recognize correctly that the predominant precipitation and lower evapotranspiration during one pumping event leads to a lower decrease of the hydrograph.</p><p>To further demonstrate the capability of the trained neural network, scenarios of pumping events are created and simulated.</p><p>In conclusion, we show that more robust and accurate predictions of moor water levels can be obtained if available physical knowledge of the modeled system is used to design and train the neural network. The artificial neural network can be a useful instrument to assess the impact of water abstraction by quantifying the anthropogenic influence.</p>

scholarly journals Using Monte Carlo method to estimate the behavior of neural training between balanced and unbalanced data in classification of patterns

2018 ◽  
Vol 7 (2) ◽  
pp. 1
Author(s):  
Paulo Marcelo Tasinaffo ◽  
Gildárcio Sousa Gonçalves ◽  
Adilson Marques da Cunha ◽  
Luiz Alberto Vieira Dias
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Mean Squared Error ◽  
Classification Problem ◽  
Computational Environment ◽  
The Monte Carlo Method ◽  
Proposed Model ◽  
Artificial Neural

This paper proposes to develop a model-based Monte Carlo method for computationally determining the best mean squared error of training for an artificial neural network with feedforward architecture. It is applied for a particular non-linear classification problem of input/output patterns in a computational environment with abundant data. The Monte Carlo method allows computationally checking that balanced data are much better than non-balanced ones for an artificial neural network to learn by means of supervised learning. The major contribution of this investigation is that, the proposed model can be tested by analogy, considering also the fraud detection problem in credit cards, where the amount of training patterns used are high.

Application of Artificial Neural Network Over Nickel-Based Catalyst for Combined Steam-Carbon Dioxide of Methane Reforming (CSDRM)

2020 ◽  
Vol 20 (9) ◽  
pp. 5716-5719 ◽  
Author(s):  
Cho Hwe Kim ◽  
Young Chul Kim
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Feed Ratio ◽  
Squared Error ◽  
Highly Nonlinear ◽  
Feed Forward Network ◽  
Artificial Neural

The application of artificial neural network (ANN) for modeling, combined steam-carbon dioxide reforming of methane over nickel-based catalysts, was investigated. The artificial neural network model consisted of a 3-layer feed forward network, with hyperbolic tangent function. The number of hidden neurons is optimized by minimization of mean square error and maximization of R2 (R square, coefficient of determination) and set of 8 neurons. With feed ratio, flow rate, and temperature as independent variables, methane, carbon dioxide conversion, and H2/CO ratio, were measured using artificial neural network. Coefficient of determination (R2) values of 0.9997, 0.9962, and 0.9985 obtained, and MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and MAPE (Mean Absolute Percentage Error) showed low value. This study indicates ANN can successfully model a highly nonlinear process and function.

Determination of Electron Optical Properties for Aperture Zoom Lenses Using an Artificial Neural Network Method

2016 ◽  
Vol 22 (2) ◽  
pp. 458-462 ◽  
Author(s):  
Nimet Isik
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Charged Particle ◽  
Mean Squared Error ◽  
Classification Method ◽  
Image Point ◽  
Charged Particle Beam ◽  
Data Set ◽  
Artificial Neural ◽  
Ann Classification

AbstractMulti-element electrostatic aperture lens systems are widely used to control electron or charged particle beams in many scientific instruments. By means of applied voltages, these lens systems can be operated for different purposes. In this context, numerous methods have been performed to calculate focal properties of these lenses. In this study, an artificial neural network (ANN) classification method is utilized to determine the focused/unfocused charged particle beam in the image point as a function of lens voltages for multi-element electrostatic aperture lenses. A data set for training and testing of ANN is taken from the SIMION 8.1 simulation program, which is a well known and proven accuracy program in charged particle optics. Mean squared error results of this study indicate that the ANN classification method provides notable performance characteristics for electrostatic aperture zoom lenses.

An Artificial Neural Network Recognition Method for Single-Phase Earth Fault of Ultra-High Voltage Power Grid

2013 ◽  
Vol 333-335 ◽  
pp. 1659-1662
Author(s):  
Hai Wei Lu ◽  
Gang Wu ◽  
Chao Xiong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Single Phase ◽  
Training Sample ◽  
Normal Operation ◽  
Recognition Method ◽  
Time Motion ◽  
Training Samples ◽  
Artificial Neural ◽  
Network Diagnosis

Fault diagnosis is very important to make the system return to normal operation quickly after an accident. This paper diagnoses the specific component failure and failure area when the real-time motion information of inputting protection and switch transferred to a trained artificial neural network model by building an artificial neural network diagnosis model of components such as transmission line, bus bar and transformer, training the artificial neural network through taking the failure rule which is found by the historic fault data as a training sample. This method has obvious advantages in the accuracy and speed of diagnosis compared with the previous artificial neural network and overcomes the shortcomings of the incompletion of training samples and not well dealing with the heuristic knowledge.

Surface Roughness Prediction in Turning of Free Machining Steel 1215 by Artificial Neural Network

2011 ◽  
Vol 188 ◽  
pp. 535-541
Author(s):  
Xiao Jiang Cai ◽  
Z.Q. Liu ◽  
Q.C. Wang ◽  
Shu Han ◽  
Qing Long An ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Mean Squared Error ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Ann Model ◽  
Roughness Prediction ◽  
Artificial Neural

Surface roughness is a significant aspect of the surface integrity concept. It is efficient to predict the surface roughness in advance by a prediction model. In this study, artificial neural network is used to model the surface roughness in turning of free machining steel 1215. The inputs considered in the prediction ANN model were cutting speed, feed rate and depth of cut, and the output was Ra. Several feed-forward neural networks with different architectures were compared in terms of prediction accuracy, and then the best prediction model, a 3-4-1-1 ANN was capable of predicting Ra with a mean squared error 5.46%, was presented.

scholarly journals SOFTWARE IMPLEMENTATION OF THE ARTIFICIAL NEURAL NETWORK FOR VIRTUAL OBJECTS СONTROL

2018 ◽  
pp. 72-78 ◽  
Author(s):  
Yu. B. Popova ◽  
S. V. Yatsynovich
Keyword(s):  
Neural Network ◽  
Mathematical Modeling ◽  
Artificial Neural Network ◽  
High Speed ◽  
Back Propagation ◽  
Computer Game ◽  
Training Sample ◽  
Software Implementation ◽  
Virtual Objects ◽  
Artificial Neural

Artificial neural networks (ANN) are now widely used in control and forecasting problems. The purpose of this work is the implementation of an artificial neural network for virtual objects control in a computer game of football. To achieve this goal, it is necessary to solve a number of problems related to mathematical modeling of ANN, algorithmization and software implementation. The paper deals with the mathematical modeling of an artificial neural network by the method of back propagation of an error, the algorithms for calculating neurons and for teaching ANN are presented. The software implementation of the artificial neural network was performed in the JavaScript language using the Node. js library, which assumed the role of a server for managing the game process. Some functions of the Underscore. js library were used to work with data arrays. The training sample consisted of more than 1000 sets of inputs and outputs, reflecting all possible situations. The results of software implementation of an artificial neural network are described on the example of virtual players control for a computer game. The results of the work show that ANN with a sufficiently high speed in real time gives the necessary direction for the player’s movement. The use of an artificial neural network has reduced the use of CPU time, which is extremely important in problems where rapid decision making is required, because complex calculations and prediction algorithms can not always be invested in 20 ms, which is fraught with skipping moves and losses. The simulated artificial neural network and the implemented algorithm of its learning can be used to solve other problems, for which only new data of the surrounding world are needed.

scholarly journals PENERAPAN MODEL BERBASIS ARTIFICIAL NEURAL NETWORK UNTUK MEMPREDIKSI KUALITAS AIR DI SUNGAI SUBAYANG KABUPATEN KAMPAR

2020 ◽  
Vol 14 (1) ◽  
pp. 18
Author(s):  
Endang Agus Damanhuri ◽  
Yusni Ikhwan Siregar ◽  
Elfizar Elfizar
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Water Flow ◽  
Mean Squared Error ◽  
Human Life ◽  
Water Quality Management ◽  
River Quality ◽  
Artificial Neural

Water quality management is very important to do, because water is an inseparable part of everyday human life. Monitoring water quality is a way to maintain the quality of waters, especially rivers. River quality monitoring that is usually done requires a lot of equipment, effort and expertise so that its application becomes expensive and complicated. Technology that is growing rapidly nowadays puts forward artificial intelligence as the backbone of the Industrial Revolution 4.0 which promises many conveniences for industry and government. One of artificial intelligence technology is machine learning with Artificial Neural Network algorithm which is commonly used to predict or forecast a future value. This artificial neural network can be used to help monitor river water quality. The objective of this research to develop Artificial Neural Networks (ANN) model to predict the paramater of river quality (DO, pH, turbidity, temperature, water flow, conductivity) in the Subayang River, Kampar Regency, using software Rapidminer. The performance of the ANN models was evaluated using root mean squared error (RMSE) and correlation squared (R2) as a second comparison, then the results of the testing implementation are compared with direct measurements in the field. With the RMSE values obtained in the test results of each parameter DO = 1.613, pH = 0.098, turbidity = 4.730, temperature = 0.493, water flow = 0.121 and conductivity = 0.909. The lower the RMSE level, the closer it is to Artificial Neural Network accuracy for value prediction.  

Sign in / Sign up

Export Citation Format

Share Document