scholarly journals Damage and Material-state Diagnostics with Predictor Functions using Data Series Prediction and Artificial Neural Networks

2020 ◽  
Author(s):  
Stefan Bosse ◽  
Edgar Kalwait
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Series ◽  
Material State ◽  
Artificial Neural ◽  
Using Data

scholarly journals Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2332
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Prediction Models ◽  
Meteorological Station ◽  
Ann Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

scholarly journals Prediction of Horizontal Daily Global Solar Irradiation Using Artificial Neural Networks (ANNs) in the Castile and León Region, Spain

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 96
Author(s):  
Francisco J. Diez ◽  
Luis M. Navas-Gracia ◽  
Leticia Chico-Santamarta ◽  
Adriana Correa-Guimaraes ◽  
Andrés Martínez-Rodríguez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Modelling ◽  
Solar Irradiation ◽  
Data Series ◽  
Markov Analysis ◽  
Global Solar Irradiation ◽  
Ann Models ◽  
Artificial Neural ◽  
Hidden Layer

This article evaluates horizontal daily global solar irradiation predictive modelling using artificial neural networks (ANNs) for its application in agricultural sciences and technologies. An eight year data series (i.e., training networks period between 2004–2010, with 2011 as the validation year) was measured at an agrometeorological station located in Castile and León, Spain, owned by the irrigation advisory system SIAR. ANN models were designed and evaluated with different neuron numbers in the input and hidden layers. The only neuron used in the outlet layer was the global solar irradiation simulated the day after. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2·d), with two inputs ([H(t), Kt(t)]) and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2·d), with two inputs ([H(t), J(t)]) and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables.

One-Day Wave Forecasts Based on Artificial Neural Networks

2006 ◽  
Vol 23 (11) ◽  
pp. 1593-1603 ◽  
Author(s):  
S. N. Londhe ◽  
Vijay Panchang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wave Model ◽  
Operational Forecasting ◽  
Artificial Neural ◽  
Wave Models ◽  
Using Data ◽  
Modeling Strategy ◽  
High Level ◽  
Real Time Simulations

Abstract Sophisticated wave models like the Wave Model (WAM) and Simulating Waves Nearshore (SWAN)/WAVEWATCH are used nowadays along with atmospheric models to produce forecasts of ocean wave conditions. These models are generally run operationally on large ocean-scale domains. In many coastal areas, on the other hand, operational forecasting is not performed for a variety of reasons, yet the need for wave forecasts remains. To address such cases, the production of forecasts through the use of artificial neural networks and buoy measurements is explored. A modeling strategy that predicts wave heights up to 24 h on the basis of judiciously selected measurements over the previous 7 days was examined. A detailed investigation of this strategy using data from six National Data Buoy Center (NDBC) buoys with diverse geographical and statistical properties demonstrates that 6-h forecasts can be obtained with a high level of fidelity, and forecasts up to 12 h showed a correlation of 67% or better relative to a full year of data. One limitation observed was the inability of the artificial neural network model to correctly predict the magnitude of the highest waves; although the occurrence of high waves was predicted, the peaks were underestimated. The inclusion of several years of data and the judicious selection of the training set, especially the inclusion of extreme events, were shown to be crucial for the model to recognize interannual variability and provide more reliable forecasts. Real-time simulations performed for April 2005 demonstrate the efficiency of this technology for operational forecasting.

Artificial Neural Networks and Its Application in Various Fields of Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Wan Nur Azah binti Wan Nahar ◽  
Rahimah binti Mohamed Yunos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Conventional Procedure ◽  
Fields Of Study ◽  
Prediction And Control ◽  
Artificial Neural ◽  
Business Engineering ◽  
Using Data ◽  
Prediction And Forecasting ◽  
And Control

Artificial Neural Networks (ANN) approach is an alternate way to classical methods. As a computation and learning paradigm, the approach is used to solve complicated practical problems in numerous fields, such as accounting and business, engineering, medical and healthcare, geological and energy. The application varies from modelling, identification, prediction, and forecasting. In contrast to conventional procedure, ANN is trained using data exemplifying the behaviour of a system. This paper presents applications of ANN in various fields of study. The applications are in the form of designing and modelling, identification and evaluation, and prediction and control. Published literature presented in this study serves as evidence that ANN is a useful tool in various disciplines across many industries. This paper will encourage researchers and professionals to explore ANN.

Prediction of short and medium term PM10 concentration using artificial neural networks

2019 ◽  
Vol 30 (2) ◽  
pp. 414-436 ◽  
Author(s):  
Elaine Schornobay-Lui ◽  
Eduardo Carlos Alexandrina ◽  
Mônica Lopes Aguiar ◽  
Werner Siegfried Hanisch ◽  
Edinalda Moreira Corrêa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Models ◽  
Urban Areas ◽  
Meteorological Data ◽  
High Volume ◽  
Data Series ◽  
Content Type ◽  
Artificial Neural ◽  
Narx Model

Purpose There has been a growing concern about air quality because in recent years, industrial and vehicle emissions have resulted in unsatisfactory human health conditions. There is an urgent need for the measurements and estimations of particulate pollutants levels, especially in urban areas. As a contribution to this issue, the purpose of this paper is to use data from measured concentrations of particulate matter and meteorological conditions for the predictions of PM10. Design/methodology/approach The procedure included daily data collection of current PM10 concentrations for the city of São Carlos-SP, Brazil. These data series enabled to use an estimator based on artificial neural networks. Data sets were collected using the high-volume sampler equipment (VFA-MP10) in the period ranging from 1997 to 2006 and from 2014 to 2015. The predictive models were created using statistics from meteorological data. The models were developed using two neural network architectures, namely, perceptron multilayer (MLP) and non-linear autoregressive exogenous (NARX) inputs network. Findings It was observed that, over time, there was a decrease in the PM10 concentration rates. This is due to the implementation of more strict environmental laws and the development of less polluting technologies. The model NARX that used as input layer the climatic variables and the PM10 of the previous day presented the highest average absolute error. However, the NARX model presented the fastest convergence compared with the MLP network. Originality/value The presentation of a given PM10 concentration of the previous day improved the performance of the predictive models. This paper brings contributions with the NARX model applications.

scholarly journals Inferring Landscape-Scale Land-Use Impacts on Rivers Using Data from Mesocosm Experiments and Artificial Neural Networks

PLoS ONE ◽  
2015 ◽  
Vol 10 (3) ◽  
pp. e0120901 ◽  
Author(s):  
Regina H. Magierowski ◽  
Steve M. Read ◽  
Steven J. B. Carter ◽  
Danielle M. Warfe ◽  
Laurie S. Cook ◽  
...  
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Artificial Neural Networks ◽  
Landscape Scale ◽  
Land Use Impacts ◽  
Mesocosm Experiments ◽  
Artificial Neural ◽  
Using Data

A finite element model updated by artificial neural networks to explain the behaviour of the Z24 Swiss bridge in different temperature states.

2021 ◽  
Author(s):  
Iason Iakovidis ◽  
Konstantinos Morfidis‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌
Keyword(s):  
Neural Networks ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Element Model ◽  
Winter Period ◽  
Fe Model ◽  
Model Update ◽  
Artificial Neural ◽  
Using Data ◽  
Physical Reasons

<p>A Finite Element (FE) model of bridge Z24 was developed to reflect its dynamic response and investigate the physical reasons behind the large variations observed on its natural modal properties during a 7-month continuous monitoring campaign conducted before its demolition in 1997. A significant increase in natural frequencies was observed especially during the winter period, something which was explained as a consequence of deck stiffness increase and boundary conditions change, due to the formation of ice layers on the deck and supports.</p><p>The paper concentrates on the procedure of developing a FE model update process, which employs Artificial Neural Networks (ANNs), which are trained using data generated through the Monte Carlo process and analysed within the FE model of the bridge. The aim of this procedure is to calibrate the FE update sensitivity parameters in such a way as to replicate the dynamic behaviour of the bridge based on real-time measured eigenvalues obtained during monitoring for five different temperature states at -10 oC, -5 oC, 0 oC, 5 oC and 10oC.</p>

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