Predictability of relative humidity by two artificial intelligence techniques using noisy data from two Californian gauging stations

Recorded time series of relative humidity (RH) are modeled by using genetic expression programming (GEP) and artificial neural networks (ANNs) models. The data are noisy and contain missing datapoints. RH is modeled as a function of three meteorological variables: temperature, wind speed, and pressure. Various model structures of both of these models are investigated with the aim of testing the robustness of the predicted values in the presence of noise and missing data. Due to the presence of noise, a sophisticated treatment of missing data was not justifiable, and therefore, the strategy adopted was just to carry the datapoints backward, although this may induce bias in the time dimension and contaminate the predicted results. The results of this study indicate that through a careful selection of model structures both GEP and ANN can produce adequately reliable prediction of RH values 1 year into the future. The paper provides evidence that this model structure is feasible when the dependent variables include both the present and past values.

[1]  A. Mellit,et al.  EPNN-based prediction of meteorological data for renewable energy systems , 2023, Journal of Renewable Energies.

[2]  L. R. Koenig,et al.  A Short Course in Cloud Physics , 1979 .

[3]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[4]  Aytac Guven,et al.  Daily pan evaporation modeling using linear genetic programming technique , 2011, Irrigation Science.

[5]  Giovanni Puglisi,et al.  Ambient temperature modelling with soft computing techniques , 2010 .

[6]  Maxwell Stevenson,et al.  Modelling and forecasting temperature based weather derivatives , 2007 .

[7]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[8]  Mustafa Gölcü,et al.  Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey , 2009 .

[9]  Sunil Agrawal,et al.  Artificial neural networks in forecasting maximum and minimum relative humidity , 2011 .

[10]  Shafiqur Rehman,et al.  Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia , 2002 .

[11]  S. Alsadi,et al.  Modeling of Relative Humidity Using Artificial Neural Network , 2012 .

[12]  J. J. García-Escalante,et al.  Calibration of a greenhouse climate model using evolutionary algorithms , 2009 .

[13]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[14]  Daniel S. Wilks,et al.  Statistical Methods in the Atmospheric Sciences: An Introduction , 1995 .

[15]  Ahmed El-Shafie,et al.  Neural Network Model for Nile River Inflow Forecasting Based on Correlation Analysis of Historical Inflow Data , 2008 .

[16]  Brad Lehman,et al.  Modeling effects of relative humidity, moisture, and extreme environmental conditions on power electronic performance , 2009, 2009 IEEE Energy Conversion Congress and Exposition.

[17]  Avinash Kumar,et al.  A Feature Based Neural Network Model for Weather Forecasting , 2007 .

[18]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[19]  Armelle Vallat,et al.  How rainfall, relative humidity and temperature influence volatile emissions from apple trees in situ. , 2005, Phytochemistry.

[20]  Ralf Kretzschmar,et al.  Neural Network Classifiers for Local Wind Prediction , 2004 .

[21]  G. King,et al.  What to Do about Missing Values in Time‐Series Cross‐Section Data , 2010 .

[22]  K. P. Sudheer,et al.  Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions , 2010, Environ. Model. Softw..

[23]  Mohammad Ali Ghorbani,et al.  Dynamics of hourly sea level at Hillarys Boat Harbour, Western Australia: a chaos theory perspective , 2011 .

[24]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[25]  I. Matyasovszky.  A nonlinear approach to modeling climatological time series , 2001 .

[26]  Tao Lu,et al.  Prediction of indoor temperature and relative humidity using neural network models: model comparison , 2009, Neural Computing and Applications.

[27]  Shaozhong Kang,et al.  Artificial neural network models for reference evapotranspiration in an arid area of northwest China , 2012 .

[28]  Mohammad Ali Ghorbani,et al.  Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks , 2010, Comput. Geosci..

[29]  Cândida Ferreira.  Gene Expression Programming in Problem Solving , 2002 .

[30]  Mohammad Ali Ghorbani,et al.  Inter-Comparison of an Evolutionary Programming Model of Suspended Sediment Time-Series with Other Local Models , 2012 .

[31]  Shuichi Hokoi,et al.  Statistical time series models of solar radiation and outdoor temperature — Identification of seasonal models by Kalman filter , 1990 .

[32]  Mohammad Ali Ghorbani,et al.  Comparison of three artificial intelligence techniques for discharge routing , 2011 .

[33]  Frédéric Fabry,et al.  Extraction of near-surface index of refraction using radar phase measurements from ground targets , 1997 .

[34]  Yuval.  Neural Network Training for Prediction of Climatological Time Series, Regularized by Minimization of the Generalized Cross-Validation Function , 2000 .