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

  • Authors:

  • Karthik Nadig;Walter Potter;Gerrit Hoogenboom;Ronald Mcclendon

  • Affiliations:

  • Institute for Artificial Intelligence, The University of Georgia, Athens, USA 30602;Computer Science, The University of Georgia, Athens, USA 30602;AgWeatherNet, Washington State University, Prosser, USA;College of Engineering, The University of Georgia, Athens, USA

  • Venue:
  • Applied Intelligence
  • Year:
  • 2013

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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聽%. 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聽%. The reduction in prediction anomalies ranged from 4.6聽% at the one-hour horizon to 60.5聽% at the eleven-hour horizon.