scholarly journals Forecasting ozone concentration levels using Box-Jenkins ARIMA modelling and artificial neural networks: A comparative study

MATEMATIKA ◽  
2017 ◽  
Vol 33 (2) ◽  
pp. 119 ◽  
Author(s):  
Norhashidah Awang ◽  
Ng Kar Yong ◽  
Soo Yin Hoeng
Keyword(s):  
Neural Network ◽  
Moving Average ◽  
Back Propagation ◽  
Network Models ◽  
Absolute Error ◽  
Percentage Error ◽  
Daily Maximum ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Concentration Levels

An accurate forecasting of tropospheric ozone (O3) concentration is beneficial for strategic planning of air quality. In this study, various forecasting techniques are used to forecast the daily maximum O3 concentration levels at a monitoring station in the Klang Valley, Malaysia. The Box-Jenkins autoregressive integrated moving-average (ARIMA) approach and three types of neural network models, namely, back-propagation neural network, Elman recurrent neural network and radial basis function neural network are considered. The daily maximum data, spanning from 1 January 2011 to 7 August 2011, was obtained from the Department of Environment, Malaysia. The performance of the four methods in forecasting future values of ozone concentrations is evaluated based on three criteria, which are root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The findings show that the Box-Jenkins approach outperformed the artificial neural network methods.

scholarly journals Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models

Materials ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 3708 ◽  
Author(s):  
In-Ji Han ◽  
Tian-Feng Yuan ◽  
Jin-Young Lee ◽  
Young-Soo Yoon ◽  
Joong-Hoon Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Experimental Studies ◽  
Back Propagation ◽  
Network Models ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Artificial Neural Network Models

A new hybrid intelligent model was developed for estimating the compressive strength (CS) of ground granulated blast furnace slag (GGBFS) concrete, and the synergistic benefits of the hybrid algorithm as compared with a single algorithm were verified. While using the collected 269 data from previous experimental studies, artificial neural network (ANN) models with three different learning algorithms namely back-propagation (BP), particle swarm optimization (PSO), and new hybrid PSO-BP algorithms, were constructed and the performance of the models was evaluated with regard to the prediction accuracy, efficiency, and stability through a threefold procedure. It was found that the PSO-BP neural network model was superior to the simple ANNs that were trained by a single algorithm and it is suitable for predicting the CS of GGBFS concrete.

scholarly journals Prediction of EV arrivals at Battery Swapping Station using Hybrid Neural Network models

2021 ◽  
Vol 2062 (1) ◽  
pp. 012021
Author(s):  
Manikanta Suri ◽  
Neha Raj ◽  
K Sireesha
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Pearson Correlation ◽  
Back Propagation ◽  
Network Models ◽  
Absolute Error ◽  
Hybrid Models ◽  
Time Data ◽  
Neural Network Models ◽  
Local Optima

Abstract There is an enormous increase in demand for Electric Vehicles (EV) in the present era, as they are environment-friendly when compared to conventional vehicles. Battery Swapping Stations (BSS) are gaining a lot of attention from the EV sector as it is like the gasoline stations. Forecasting of EV arrivals at BSS helps in optimally scheduling the depleted batteries to different charging piles without affecting the State of Health of the battery. Back Propagation Neural Network (BPNN) is widely used in the prediction of real-time data. Training of BPNN using metaheuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) helps to overcome the local optima problem in BPNN. Thus, in the present work forecasting on the EV arrivals is carried out using GA-BPNN and PSO-BPNN hybrid models. Finally, a comparative study is carried out among BPNN, GA-BPNN, and PSO-BPNN models using the performance metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE) and Pearson Correlation Coefficient (PCC). From the results, it was obtained that GA-BPNN model is preferred in forecasting the EV arrivals at BSS as the model has less overfitting. The hybrid models have been simulated in MATLAB/Simulink software.

scholarly journals Thrice Filtered Information Energy Based Particle Swarm Feature Selection (TFIE-PSFS) Method Based Artificial Neural Network Classification for Improving Heart Disease Diagnosis

2019 ◽  
Vol 8 (1) ◽  
pp. 1-8
Author(s):  
P. Umasankar ◽  
V. Thiagarasu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Back Propagation ◽  
Network Models ◽  
Disease Diagnosis ◽  
Stress Tests ◽  
Medical Statistics ◽  
Neural Network Models ◽  
Artificial Neural

Diagnosing the existence of heart disease is really tedious process, as it entails deep knowledge and opulent experience. As a whole, the forecast of heart disease lies upon the conventional method of analysing medical report such as ECG (The Electrocardiogram), MRI (Magnetic Resonance Imaging), Blood Pressure, Stress tests by a Medicinal expert. Nowadays, a large volume of medical statistics is obtainable in medical industry and turns as a excessive source of forecasting valuable and concealed facts in almost all medical complications. Thus, these facts would really aid the doctors to create exact predictions. The innovative methods of Artificial Neural Network models have also been contributing themselves in yielding the main prediction accuracy over medical statistics. This paper targets to predict the presence of heart disease utilizing Back Propagation MLP (Multilayer Perceptron) of Artificial Neural Network. The proposed ANN design targeted to generate the three outputs Yes (Patient having heart disease), No (Patient not having heart disease), and Hesitant (Patient those who are in between yes and no category).

scholarly journals PRONÓSTICO DE CAUDALES MEDIOS MENSUALES DEL RIO ILAVE USANDO MODELOS DE REDES NEURONALES ARTIFICIALES

2014 ◽  
Vol 16 (01) ◽  
Author(s):  
EFRAIN LUJANO LAURA ◽  
APOLINARIO LUJANO ◽  
JOSÉ PITÁGORAS QUISPE ◽  
RENÉ LUJANO
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Network Models ◽  
Mean Square ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Del Rio ◽  
Artificial Neural Network Models

<h4 class="text-primary">Resumen</h4><p style="text-align: justify;">La presente investigación se realizó en la cuenca del río Ilave, ubicado dentro de la región Hidrográfica del Titicaca (Perú), teniendo como objetivo pronosticar los caudales medios mensuales del rio Ilave usando Modelos de Redes Neuronales Artificiales, aplicado al problema del pronóstico mensual de esta variable, cuyo resultado puede emplearse en la planificación y gestión de los recursos hídricos en cuencas hidrográficas. La información hidrometeorológica utilizada, corresponde al Servicio Nacional de Meteorología e Hidrología con un periodo de registro de 1965 al 2007, de donde se plantearon 06 modelos que están en función de precipitaciones y caudales, cuya fase de entrenamiento, validación y prueba, se realizaron con el 70%, 15% y 15% del total de datos respectivamente, con una red de entrenamiento designada Perceptrón Multicapa (MLP) y el algoritmo «back-propagatión». La significación estadística de los indicadores de desempeño de eficiencia de Nash-Sutcliffe (NSE) y la raíz del error cuadrático medio (RMSE), fueron evaluados usando el método de bootstrap incorporado en el código FITEVAL y como indicadores complementarios de evaluación tradicional, el coeficiente de determinación (R2) y el error cuadrático medio normalizado (ECMN). Los resultados de validación y prueba indican calificativos de buenos a muy buenos, así tenemos que en la fase de pronóstico para los modelos seleccionados MRNA5, MRNA2 y MRNA3, los coeficientes de Eficiencia de Nash-Sutcliffe son de 88.0%, 87.9% y 87.1%; la raíz del error medio cuadrático son de 18.87%, 18.96% y 19.56% respectivamente. Se concluye que el pronóstico de caudales medios mensuales del río Ilave utilizando modelos de Redes Neuronales Artificiales, muestran un buen desempeño en la estimación de fenómenos de comportamiento no lineal como los caudales.</p><p><strong>PALABRAS CLAVE: </strong>* Backpropagation * caudales medios * redes neuronales artificiales río * Ilave</p><h4 class="text-primary">ABSTRACT</h4><p><strong>AVERAGE FLOW-MONTHLY FORECAST OF THE ILAVE RIVER USING ARTIFICIAL NEURAL NETWORK MODELS</strong></p><p style="text-align: justify;">This research was conducted in the Ilave river basin located within the hydrographic region of Titicaca (Peru), aiming to predict the average monthly flow of the river Ilave usingArtificial Neural Networks models applied to forecast the monthly variable flow of this river. The results of this type of forecasting can be used in the planning and management of water resources in river basins. The hydrometeorological information used, corresponds to the National Meteorological and Hydrological Service registries between 1965 – 2007. 06 models were proposed that are based on rainfall and river flow, whose training, validation and testing phases were realized with 70%, 15% and 15% of the total data respectively. A training network titled Multilayer Perception (MLP) as well as algorithm and «back -propagation»techniques were used. The statistical significance of the performance indicators Nash (NSE) and the Root Mean Square Error (RMSE), were assessed using the bootstrap method incorporated in the FITEVAL code. The coefficient of determination (R2) and Normalized Root Mean Square Error (NRMSE) were used as complementary to indicators of traditional assessment. The results of test descriptions and validation indicate good to very good results, so in the forecast phase for selected models MRNA5, MRNA2 and MRNA3, Nash coefficients are 88.0%, 87.9% and 87.1%; mean square root error are 18.87%, 18.96% and 19.56% respectively. We conclude that the average monthly flow forecast of the river Ilave, using Artificial Neural Network models, show a good performance in estimating nonlinear phenomena such as flow behavior.</p><p><strong>KEY WORDS: </strong>* artificial neural networks * back propagation * Ilave river * mean flows</p>

scholarly journals Prediction of scour depth at culvert outlets using neural networks

2001 ◽  
Vol 3 (4) ◽  
pp. 231-238 ◽  
Author(s):  
S. L. Liriano ◽  
R. A. Day
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Network Models ◽  
Limited Range ◽  
Scour Depth ◽  
Prediction Equations ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Models

Scour at culvert outlets is a phenomenon encountered world-wide. Research into the problem has mainly been of an experimental nature, with equations being derived for particular circumstances. These traditional scour prediction equations, although offering the engineer some guidance on the likely magnitude of maximum scour depth, are applicable only to a limited range of situations. A model for the prediction of scouring that is generally applicable to all circumstances is not currently available. However, there is a substantial amount of data available from research over many years in this area. This paper compares current prediction equations with results obtained from two Artificial Neural Network models (ANN). The development of a basic feed forward artificial neural network trained by back-propagation to model scour downstream of culvert outlets is described. A supervised training algorithm is used with data collected from published studies and the authors' own experimental work. The results show that the ANN can successfully predict the depth of scour with a greater accuracy than existing empirical formulae and over a wider range of conditions.

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals PSIII-5 Comparison of Bivariate Machine Learning and Linear Model for Genomic Prediction with Different Heritability, QTL and SNP Panel Scenarios

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 230-231
Author(s):  
Sunday O Peters ◽  
Mahmut Sinecan ◽  
Kadir Kizilkaya ◽  
Milt Thomas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Normal Distribution ◽  
Genomic Prediction ◽  
Network Models ◽  
Bivariate Normal Distribution ◽  
Snp Markers ◽  
Neural Network Models ◽  
Bivariate Normal ◽  
Artificial Neural

Abstract This simulation study used actual SNP genotypes on the first chromosome of Brangus beef cattle to simulate 0.50 genetically correlated two traits with heritabilities of 0.25 and 0.50 determined either by 50, 100, 250 or 500 QTL and then aimed to compare the accuracies of genomic prediction from bivariate linear and artificial neural network with 1 to 10 neurons models based on G genomic relationship matrix. QTL effects of 50, 100, 250 and 500 SNPs from the 3361 SNPs of 719 animals were sampled from a bivariate normal distribution. In each QTL scenario, the breeding values (Σgijβj) of animal i for two traits were generated by using genotype (gij) of animal i at QTL j and the effects (βj) of QTL j from a bivariate normal distribution. Phenotypic values of animal i for traits were generated by adding residuals from a bivariate normal distribution to the breeding values of animal i. Genomic predictions for traits were carried out by bivariate Feed Forward MultiLayer Perceptron ANN-1–10 neurons and linear (GBLUP) models. Three sets of SNP panels were used for genomic prediction: only QTL genotypes (Panel1), all SNP markers, including the QTL (Panel2), and all SNP markers, excluding the QTL (Panel3). Correlations from 10-fold cross validation for traits were used to assess predictive ability of bivariate linear (GBLUP) and artificial neural network models based on 4 QTL scenarios with 3 Panels of SNP panels. Table 1 shows that the trait with high heritability (0.50) resulted in higher correlation than the trait with low heritability (0.25) in bivariate linear (GBLUP) and artificial neural network models. However, bivariate linear (GBLUP) model produced higher correlation than bivariate neural network. Panel1 performed the best correlations for all QTL scenarios, then Panel2 including QTL and SNP markers resulted in better prediction than Panel3.

Automatic Electrocardiographic Analysis Using Artificial Neural Network Models

2011 ◽  
Vol 403-408 ◽  
pp. 3587-3593
Author(s):  
T.V.K. Hanumantha Rao ◽  
Saurabh Mishra ◽  
Sudhir Kumar Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Models ◽  
Neural Network Models ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Self Organizing

In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.

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