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

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Abstract

Predicted air and dew point temperatures can be valuable in decision making in many areas including protecting crops from damage, avoiding heat stress on animals and humans, and in planning related to energy management. Current web-based artificial neural network (ANN) models on the Automated Environment Monitoring Network (AEMN) in Georgia predict hourly air and dew point temperature for twelve prediction horizons, using 24 models. The observed air temperature may approach the observed dew point temperature, but never goes below it. Current web based ANN models have prediction errors which, when the air and dew point temperatures are close, may cause air temperature to be predicted below the dew point temperature. Herein this error is referred to as a prediction anomaly. The goal of this research was to improve the prediction accuracy of existing air and dew point temperature ANN models by combining the two weather variables into a single ANN model for each prediction horizon. The objectives of this study were to reduce the mean absolute error (MAE) of prediction and to reduce the number of prediction anomalies. The combined models produced a reduction in the air temperature MAE for ten of twelve prediction horizons with an average reduction in MAE of 1.93 %. The combined models produced a reduction in the dew point temperature MAE for only six of twelve prediction horizons with essentially no average decrease in MAE. However, the combined models showed a marked reduction in prediction anomalies for all twelve prediction horizons with an average reduction of 34.1 %. The reduction in prediction anomalies ranged from 4.6 % at the one-hour horizon to 60.5 % at the eleven-hour horizon.

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Acknowledgements

This work was funded by a partnership between the USDA-Federal Crop Insurance Corporation through the Risk Management Agency and the University of Georgia and by state and federal funds allocated to Georgia Agricultural Experiment Stations Hatch projects GEO00877 and GEO01654.

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Authors and Affiliations

  1. Institute for Artificial Intelligence, The University of Georgia, Athens, GA, 30602, USA

    Karthik Nadig

  2. Computer Science, The University of Georgia, Athens, GA, 30602, USA

    Walter Potter

  3. AgWeatherNet, Washington State University, Prosser, WA, USA

    Gerrit Hoogenboom

  4. College of Engineering, The University of Georgia, Athens, GA, USA

    Ronald McClendon

Authors
  1. Karthik Nadig
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  2. Walter Potter
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  3. Gerrit Hoogenboom
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  4. Ronald McClendon
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Correspondence to Karthik Nadig.

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Nadig, K., Potter, W., Hoogenboom, G. et al. Comparison of individual and combined ANN models for prediction of air and dew point temperature. Appl Intell 39, 354–366 (2013). https://doi.org/10.1007/s10489-012-0417-1

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