scholarly journals An Objective Method to Modify Numerical Model Forecasts with Newly Given Weather Data Using an Artificial Neural Network

1999 ◽  
Vol 14 (1) ◽  
pp. 109-118 ◽  
Author(s):  
Ko Koizumi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Numerical Model ◽  
Weather Data ◽  
Objective Method ◽  
Artificial Neural

Weather Forecasting Using Artificial Neural Network and Bayesian Network

2014 ◽  
Vol 18 (5) ◽  
pp. 812-817 ◽  
Author(s):  
Klent Gomez Abistado ◽  
◽  
Catherine N. Arellano ◽  
Elmer A. Maravillas ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bayesian Network ◽  
Weather Forecasting ◽  
Weather Conditions ◽  
Dew Point ◽  
Weather Data ◽  
Maximum Temperature ◽  
Prevailing Wind Direction ◽  
Artificial Neural

This paper presents a scheme of weather forecasting using artificial neural network (ANN) and Bayesian network. The study focuses on the data representing central Cebu weather conditions. The parameters used in this study are as follows: mean dew point, minimum temperature, maximum temperature, mean temperature, mean relative humidity, rainfall, average wind speed, prevailing wind direction, and mean cloudiness. The weather data were collected from the PAG-ASA Mactan-Cebu Station located at latitude: 10°19´, longitude: 123°59´ starting from January 2011 to December 2011 and the values available represent daily averages. These data were used for training the multi-layered backpropagation ANN in predicting the weather conditions of the succeeding days. Some outputs from the ANN, such as the humidity, temperature, and amount of rainfall, are fed to the Bayesian network for statistical analysis to forecast the probability of rain. Experiments show that the system achieved 93%–100% accuracy in forecasting weather conditions.

scholarly journals Spatio-temporal prediction of suspended sediment concentration in the coastal zone using an artificial neural network and a numerical model

2012 ◽  
Vol 14 (3) ◽  
pp. 574-584 ◽  
Author(s):  
B. Bhattacharya ◽  
T. van Kessel ◽  
D. P. Solomatine
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Numerical Model ◽  
Series Data ◽  
Ann Model ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Temporal Prediction ◽  
Artificial Neural

A problem of predicting suspended particulate matter (SPM) concentration on the basis of wind and wave measurements and estimates of bed shear stress done by a numerical model is considered. Data at a location at 10 km offshore from Noordwijk in the Dutch coastal area is used. The time series data have been filtered with a low pass filter to remove short-term fluctuations due to noise and tides and the resulting time series have been used to build an artificial neural network (ANN) model. The accuracy of the ANN model during both storm and calm periods was found to be high. The possibilities to apply the trained ANN model at other locations, where the model is assisted by the correctors based on the ratio of long-term average SPM values for the considered location to that for Noordwijk (for which the model was trained), have been investigated. These experiments demonstrated that the ANN model's accuracy at the other locations was acceptable, which shows the potential of the considered approach.

Performance Prediction of Pulse Tube Refrigerator Using Artificial Neural Network

2014 ◽  
Vol 984-985 ◽  
pp. 1147-1149
Author(s):  
Pankaj Kumar ◽  
Sachindra Ku Rout ◽  
Ajay Ku Gupta ◽  
Rajit Ku Sahoo ◽  
Sunil Ku Sarangi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Numerical Model ◽  
Process Model ◽  
Fluid Dynamic ◽  
Coefficient Of Performance ◽  
Pulse Tube ◽  
Time Saving ◽  
Pulse Tube Refrigerator ◽  
Artificial Neural

The present study proposes a numerical model to analyze the effect of four dimensional parameters on performance characteristics such as Coefficient of performance (COP), of the Inertance-Type Pulse Tube Refrigerator (ITPTR). The numerical model is validated by comparing with previously published results. The detail analysis of cool down behaviour, heat transfer at the cold end and the pressure variation inside the whole system has been carried out by using the most powerful computational fluid dynamic software package ANSYS FLUENT 13. The operating frequency for all the studied cases is (34 Hz). In fact, to get an optimum parameter experimentally is a very tedious for iterance pulse tube refrigerator job, so that the CFD approach gives a better solution. Finally, an artificial neural network (ANN) based process model is proposed to establish relation between input parameters and the responses. The model provides an inexpensive and time saving substitute to study the performance of ITPTR. The model can be used for selecting ideal process states to improve ITPTR performance.

scholarly journals Improving prediction of aphid flights by temporal analysis of input data for an artificial neural network

2002 ◽  
Vol 55 ◽  
pp. 312-316 ◽  
Author(s):  
S.P. Worner ◽  
G.O. Lankin ◽  
S. Samarasinghe ◽  
D.A.J. Teulon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Model Development ◽  
Temporal Analysis ◽  
Weather Data ◽  
Flight Period ◽  
Noisy Information ◽  
Artificial Neural ◽  
Trap Catches

Weather data in its raw form frequently contains irrelevant and noisy information Often the hardest task in model development regardless of the technique used is translating independent variables from their raw form into data relevant to a particular model A sequential or cascading temporal correlation analysis was used to identify weather sequences that were strongly correlated with aphid trap catches recorded at Lincoln Canterbury New Zealand over 19822000 Trap catches in the previous year and 13 weather sequences associated with eight climate variables were identified as significant predictors of aphid trap catch during the autumn flight period The variables were used to train artificial neural network (ANN) models to predict the size of autumn aphid migrations into cereal crops in Canterbury Such models would assist cereal growers to make better informed and more timely pest management decisions ANN predictive performance was compared with multiple regression predictions using jackknifed data The ANN gave superior prediction compared with multiple regression over 13 jackknifed years

Prediction of angular distortion due GMAW process of thin-sheets Hardox 450® steel by numerical model and artificial neural network

2021 ◽  
Vol 68 ◽  
pp. 1202-1213
Author(s):  
Cristian Rubio-Ramirez ◽  
Daniela F. Giarollo ◽  
José E. Mazzaferro ◽  
Cíntia Petry Mazzaferro
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Numerical Model ◽  
Angular Distortion ◽  
Thin Sheets ◽  
Artificial Neural
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