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>

Comparing Conventional and Artificial Neural Network Models for the Pricing of Options

2002 ◽  
pp. 220-235 ◽  
Author(s):  
Paul Lajbcygier
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Parameter ◽  
Network Models ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Conventional Models ◽  
Artificial Neural Network Models

The pricing of options on futures is compared using conventional models and artificial neural networks. This work demonstrates superior pricing accuracy using the artificial neural networks in an important subset of the input parameter set.

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 Automatic migraine classification using artificial neural networks

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 618
Author(s):  
Paola A. Sanchez-Sanchez ◽  
José Rafael García-González ◽  
Juan Manuel Rúa Ascar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Neural Network Models ◽  
Accuracy And Precision ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Selection Of

Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients’ health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient’s symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The artificial neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through artificial neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.

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 Photovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and Sanliurfa

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Yasin Icel ◽  
Mehmet Salih Mamis ◽  
Abdulcelil Bugutekin ◽  
Mehmet Ismail Gursoy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Estimation Accuracy ◽  
Neural Network Models ◽  
Photovoltaic Panels ◽  
Artificial Neural ◽  
Artificial Neural Network Models

The amount of electric energy produced by photovoltaic panels depends on air temperature, humidity rate, wind velocity, photovoltaic module temperature, and particularly solar radiation. Being aware of the behaviour patterns of the panels to be used in project and planning works regarding photovoltaic applications will set forth a realistic expense form; therefore, erroneous investments will be avoided, and the country budget will benefit from added value. The power ratings obtained from the photovoltaic panels and the environmental factors were measured and recorded for a year by the measurement stations established in three diverse regions (Adiyaman-Malatya-Sanliurfa). In the developed artificial neural network models, the estimation accuracy was 99.94%. Furthermore, by taking the data of the General Directorate of Meteorology as a reference, models of artificial neural networks were developed using the data from Adiyaman province for training; by using Malatya and Sanliurfa as test data, 99.57% estimation accuracy was achieved. With the artificial neural network models developed as a result of the study, the energy efficiency for the photovoltaic energy systems desired to be established by using meteorological parameters such as temperature, humidity, wind, and solar radiation of various regions anywhere in the world can be estimated with high accuracy.

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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