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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References
Anastasiadis AD, Magoulas GD, Vrahatis MN (2005) New globally convergent training scheme based on the resilient propagation algorithm. Neurocomputing 64:253–270
Attaway JA (1997) A history of Florida Citrus freezes. Florida Science Source, Inc, Lake Alfred
Boatright SR, McKissick JC (2008) 2007 Georgia farm gate value report. Center for Agribusiness and Economic Development. The University of Georgia. AR-08-01
Chevalier R, Hoogenboom G, McClendon R, Paz J (2011) Support vector regression with reduced training sets for air temperature prediction: a comparison with artificial neural networks. Neural Comput Appl 20:151–159
Chevalier R, Hoogenboom G, McClendon R, Paz J (2012) A web-based fuzzy expert system for frost warnings in horticultural crops. Environ Model Softw 35:84–91
Cooper WC, Young RH, Turrell FM (1964) Microclimate and physiology of citrus: their relation to cold protection. Agric Sci Rev 2(1):38–50
Fauci A (2008) Harrison’s principles of internal medicine. McGraw-Hill, New York
Fonsah EG, Taylor KC, Funderburk F (2007) Enterprise cost analysis for middle. Technical report, AGECON-06-118
Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Model 160:249–264
Grundstein A, Ramseyer C, Zhao F, Pesses J, Akers P, Qureshi A et al (2012) A retrospective analysis of American football hyperthermia deaths in the United States. Int J Biometeorol 56:11–20
Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Upper Saddle River
Hochmuth GJ, Kostewicz S, Martin F (1993) Irrigation method and Rowcover use for strawberry freeze protection. J Am Soc Hortic Sci 118:575–579
Hoogenboom G (1993) The Georgia automated environmental monitoring network. In: Hatcher K (ed) Proceedings of the 1993 Georgia water resources conference, The University of Georgia, Athens, pp 398–402
Hoogenboom G (2000) The Georgia automated environmental monitoring network. In: Preprints of the 24th conference on agricultural and forest meteorology, pp 24–25. American Meteorological Society, Boston
Hoogenboom G (2005) The Georgia automated environmental monitoring network: experiences with the development of a state-wide automated weather station network. In: Proceedings of the 13th symposium on meteorological observations and instrumentation & 15th conference on applied climatology. American Society of Meteorology, Boston
Hoogenboom G, Coker DD, Edenfield JM, Evans DM, Fang C (2003) The Georgia automated environmental monitoring network: ten years of weather information for water resources management. In: Proceedings of the 2003 Georgia water resources conference
Igel C, Hüsken M (2003) Empirical evaluation of the improved RPROP learning algorithms. Neurocomputing 50:105–123
Jain A (2003) Frost prediction using artificial neural networks: a temperature prediction approach. M.S. thesis
Jain A, McClendon RW, Hoogenboom G, Ramyaa R (2003) Prediction of frost for fruit protection using artificial neural networks. Am Soc Agric Eng 03:3075
Jones GM, Stallings CC (1999) Reducing heat stress for dairy cattle. Virginia Cooperative Extension, Blacksburg
Martsolf JD, Gerber JF, Chen EY, Jackson HL, Rose AJ (1984) What do satellite and other data suggest about past and future Florida freezes. In: Proceedings of the Florida state horticultural society, pp 17–21
Narendra KS, Mukhopadhyay S (1994) Adaptive control of nonlinear multivariable systems using neural networks. Decis Control 7(5):737–752
Perry KB (1994) Frost/freeze protection for horticultural crops. North Carolina cooperative extension service. Leaflet No: 705-A
Perry KB (1998) Basics of frost and freeze protection for horticultural crops, 8th edn. HortTechnology, Alexandria
Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE international conference on neural networks, pp 586–591
Robinson PJ (2000) Temporal trends in United States dew point temperatures. Int J Climatol 20(9):985–1002
Rodrigo J (2000) Spring frosts in deciduous fruit trees—morphological damage and flower hardiness. Sci Hortic 85:155–173
Sandstrom MA, Lauritsen RG, Changnon D (2004) A Central-US summer extreme dew-point climatology (1949–2000). Phys Geogr 25(3):191–207
Shank DB, McClendon RW, Paz JA (2008) Ensemble artificial neural networks for prediction of dew point temperature. Appl Artif Intell 22(6):523–542
Shank D, Hoogenboom G, McClendon R (2008) Dewpoint temperature prediction using artificial neural networks. J Appl Meteorol Climatol 47:1757
Smith BA, Hoogenboom G, McClendon RW (2009) Artificial neural networks for automated year-round temperature prediction. Comput Electron Agric 68:52–61
Smith BA, McClendon RW, Hoogenboom G (2008) Improving air temperature prediction with artificial neural networks. Int J Comput Intell 3(3):179–186
Ward System Group (1993) Manual of NeuroShell 2. Ward System Group, Frederick
Warmund MR, Guinan P, Fernandez G (2008) Temperatures and cold damage to small fruit crops across the Eastern United States associated with the April 2007 freeze. HortScience 43(6):1643–1647
White GF, Haas HE (1975) Assessment of research on natural hazards. MIT Press, Cambridge
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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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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DOI: https://doi.org/10.1007/s10489-012-0417-1