ENSEMBLE ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF DEW POINT TEMPERATURE

Authors:
D. B. Shank;R. W. McClendon;J. Paz;G. Hoogenboom
Affiliations:
Artificial Intelligence Center, University of Georgia, Athens, Georgia, USA;Artificial Intelligence Center, University of Georgia, Athens, Georgia, USA,Department of Biological and Agricultural Engineering, Driftmier Engineering Center, University of Georgia, Athens, Geor ...;Department of Biological and Agricultural Engineering, University of Georgia, Griffin, Georgia, USA;Artificial Intelligence Center, University of Georgia, Athens, Georgia, USA,Department of Biological and Agricultural Engineering, University of Georgia, Griffin, Georgia, USA
Venue:
Applied Artificial Intelligence
Year:
2008

Citing 4
Cited 5

Prediction of extreme precipitation using a neural network: application to summer flood occurence in Moravia

Advances in Engineering Software
An ensemble of neural networks for weather forecasting

Neural Computing and Applications
Neural network ensembles: evaluation of aggregation algorithms

Artificial Intelligence
Weather prediction using fuzzy sets and inference methodology

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Artificial neural networks for automated year-round temperature prediction

Computers and Electronics in Agriculture
A web-based fuzzy expert system for frost warnings in horticultural crops

Environmental Modelling & Software
Hybrid thermal model for swimming pools based on artificial neural networks for southeast region of Brazil

Expert Systems with Applications: An International Journal
A genetic algorithm to refine input data selection for air temperature prediction using artificial neural networks

Applied Soft Computing
Comparison of individual and combined ANN models for prediction of air and dew point temperature

Applied Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Dew point temperature is needed as an input to calculate various meteorological variables. In general, it contributes to human and animal comfort levels. The goal of this study was to develop artificial neural network (ANN) models for dew point temperature prediction to improve upon previous research. These improvements included optimizing the stopping criteria, comparing seasonal models to year-round models, and developing ensemble ANNs to blend the output of seasonal models. For an ANN trained with 100,000 patterns per epoch, the error was reduced using a 2000-pattern stopping dataset at an interval of 20 learning events to decide when to stop training. Seasonal ANN models were blended in an ensemble ANN with the weight of the member networks determined using a fuzzy membership-type function based on the day of year. These ensemble models were shown to produce lower errors than year-round, nonensemble models. The mean absolute errors (MAEs) of the final models evaluated with an independent evaluation dataset included 0.795°C for a 2-hour prediction, 1.485°C for a 6-hour prediction, and 2.146°C for a 12-hour prediction. The final model MAEs, when compared to the previous research, were reduced by 0.008°C, 0.081°C, and 0.135°C, respectively. It can be concluded that the methods used in this research were effective in more accurately predicting year-round dew point temperature. The ANN models for different prediction periods were sequenced to provide a 12-hour dew point temperature prediction system for implementation on the Georgia Automated Environmental Monitoring Network website (www.georgiaweather.net).