Development of relative humidity models by using optimized neural network structures

  • A. Martinez-Romero Centro Regional de Estudios del Agua (CREA). Universidad de Castilla-La Mancha.
  • J. F. Ortega Centro Regional de Estudios del Agua (CREA). Universidad de Castilla-La Mancha.
  • J. A. de-Juan Centro Regional de Estudios del Agua (CREA). Universidad de Castilla-La Mancha.
  • J. M. Tarjuelo Centro Regional de Estudios del Agua (CREA). Universidad de Castilla-La Mancha.
  • M. A. Moreno Centro Regional de Estudios del Agua (CREA). Universidad de Castilla-La Mancha.
Keywords: artificial neural networks, climate data, limited data

Abstract

Climate has always had a very important role in life on earth, as well as human activity and health. The influence of relative humidity (RH) in controlled environments (e.g. industrial processes in agro-food processing, cold storage of foods such as fruits, vegetables and meat, or controls in greenhouses) is very important. Relative humidity is a main factor in agricultural production and crop yield (due to the influence on crop water demand or the development and distribution of pests and diseases, for example). The main objective of this paper is to estimate RH [maximum (RHmax), average (RHave), and minimum (RHmin)] data in a specific area, being applied to the Region of Castilla-La Mancha (C-LM) in this case, from available data at thermo-pluviometric weather stations. In this paper Artificial neural networks (ANN) are used to generate RH considering maximum and minimum temperatures and extraterrestrial solar radiation data. Model validation and generation is based on data from the years 2000 to 2008 from 44 complete agroclimatic weather stations. Relative errors are estimated as 1) spatial errors of 11.30%, 6.80% and 10.27% and 2) temporal errors of 10.34%, 6.59% and 9.77% for RHmin, RHmax and RHave, respectively. The use of ANNs is interesting in generating climate parameters from available climate data. For determining optimal ANN structure in estimating RH values, model calibration and validation is necessary, considering spatial and temporal variability.

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Published
2010-11-12
How to Cite
Martinez-Romero, A., Ortega, J. F., de-Juan, J. A., Tarjuelo, J. M., & Moreno, M. A. (2010). Development of relative humidity models by using optimized neural network structures. Spanish Journal of Agricultural Research, 8(S2), 162-171. https://doi.org/10.5424/sjar/201008S2-1359

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