Multi-output ANN Model for Prediction of Seven Meteorological Parameters in a Weather Station

  • Khalid Raza
  • V. Jothiprakash
Original Contribution


The meteorological parameters plays a vital role for determining various water demand in the water resource systems, planning, management and operation. Thus, accurate prediction of meteorological variables at different spatial and temporal intervals is the key requirement. Artificial Neural Network (ANN) is one of the most widely used data driven modelling techniques with lots of good features like, easy applications, high accuracy in prediction and to predict the multi-output complex non-linear relationships. In this paper, a Multi-input Multi-output (MIMO) ANN model has been developed and applied to predict seven important meteorological parameters, such as maximum temperature, minimum temperature, relative humidity, wind speed, sunshine hours, dew point temperature and evaporation concurrently. Several types of ANN, such as multilayer perceptron, generalized feedforward neural network, radial basis function and recurrent neural network with multi hidden layer and varying number of neurons at the hidden layer, has been developed, trained, validated and tested. From the results, it is found that the recurrent MIMO-ANN having 28 neurons in a single hidden layer, trained using hyperbolic tangent transfer function with a learning rate of 0.3 and momentum factor of 0.7 performed well over the other types of MIMO-ANN models. The MIMO ANN model performed well for all parameters with higher correlation and other performance indicators except for sunshine hours. Due to erratic nature, the importance of each of the input over the output through sensitivity analysis indicated that relative humidity has highest influence while others have equal influence over the output.


Time series model Meteorological parameter prediction Recurrent neural network Multi-input multi-output (MIMO) 



The authors would like to thank Indian Academy of Sciences, Bangalore for providing fellowship to carry out this research through Summer Internship Program at Indian Institute of Technology Bombay, Mumbai 400 076.


  1. 1.
    V. Jothiprakash, S. Kirty, M.S. Tara, Prediction of meteorological variables using artificial neural networks. Int. J. Hydrol. Sci. Technol. 1(3/4): 192–206 (2011)Google Scholar
  2. 2.
    Z. Izadifar, A. Elshorbagy, Prediction of hourly actual evapotranspiration using neural networks, genetic programming, and statistical models. Hydrol. Process. 24(23), 3413–3425 (2010)CrossRefGoogle Scholar
  3. 3.
    G. Box, G. Jenkins, Time Series Analysis Forecasting and Control, 2nd edn. (Holden-Day, San Francisco, California, 1976)MATHGoogle Scholar
  4. 4.
    M. Hamdi, A. Bdour, Developing reference crop evapotranspiration time series simulation model using class a pan: a case study for the Jordan Valley/Jordan. Earth Environ. Sci. 1(1), 33–44 (2008)Google Scholar
  5. 5.
    M.A. Kulkarni, S. Patil, G.V. Rama, P.N. Sen, Wind speed prediction using statistical regression and neural network. J. Earth Syst. Sci. 117(4), 457–463 (2008)CrossRefGoogle Scholar
  6. 6.
    A. Sfetsos, A novel approach for the forecasting of mean hourly wind speed time series. Renew. Energy 27(2), 163–174 (2002)CrossRefGoogle Scholar
  7. 7.
    M. Tektaş, Weather forecasting using ANFIS and ARIMA MODELS. A case study for Istanbul. Environ. Res. Eng. Manag. 1(51), 5–10 (2010)Google Scholar
  8. 8.
    ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, Artificial neural networks in hydrology I: preliminary concepts. J. Hydrol. Eng. 5(2), 115–123 (2000a)Google Scholar
  9. 9.
    ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, Artificial neural networks in hydrology II: hydrologic applications. J. Hydrol. Eng. 5(2), 124–137 (2000b)Google Scholar
  10. 10.
    H.R. Maier, G.C. Dandy, Neural networks for the prediction and forecasting of water resources variables : a review of modelling issues and applications. Environ. Model Softw. 15, 101–124 (2000)CrossRefGoogle Scholar
  11. 11.
    C.W. Dawson, Hydrological modelling using artificial neural networks. Prog. Phys. Geogr. 25(1), 80–108 (2001)CrossRefGoogle Scholar
  12. 12.
    M.W. Gardner, S.R. Dorling, Artificial neural networks (the multilayer perceptron): a review of applications in the atmospheric sciences. Atmos. Environ. 32(14–15), 2627–2636 (1998)CrossRefGoogle Scholar
  13. 13.
    V.R. Prybutok, Junsub Yi, D. Mitchell, Comparison of neural network models with ARIMA and regression models for prediction of Houston’s daily maximum ozone concentrations. Eur. J. Oper. Res. 122(1), 31–40 (2000)CrossRefMATHGoogle Scholar
  14. 14.
    S. Roy, Prediction of particulate matter concentrations using artificial neural network. Resour. Environ. 2(2): 30–36 (2012)Google Scholar
  15. 15.
    K. Sudheer, A.K. Gosain, K. Ramasastri, Estimating actual evapotranspiration from limited climatic data using neural computing technique. J. Irrig. Drain. (May/June), 214–218 (2003)Google Scholar
  16. 16.
    K.P. Sudheer, A.K. Gosain, D. Mohana Rangan, S.M. Saheb, Modelling evaporation using an artificial neural network algorithm. Hydrol. Process. 16(16), 3189–3202 (2002)CrossRefGoogle Scholar
  17. 17.
    V. Jothiprakash, M.R. RamaChandran, P. Shanmuganathan, Artificial neural network model for estimation of REF-ET. J. Inst. Eng India 83(Jun), 17–20 (2002)Google Scholar
  18. 18.
    A. More, M.C. Deo, Forecasting wind with neural networks. Mar. Struct. 16(1), 35–49 (2003)CrossRefGoogle Scholar
  19. 19.
    A. Moghaddamnia, M. Ghafari Gousheh, J. Piri, S. Amin, D. Han, Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv. Water Resour. Elsevier Ltd 32(1), 88–97 (2009)CrossRefGoogle Scholar
  20. 20.
    S.S. De, A. Debnath, Artificial neural network based prediction of maximum and minimum temperature in the summer monsoon months over India. Appl. Phys. Res. 1(2), 37–44 (2009)CrossRefGoogle Scholar
  21. 21.
    S. Darbandi, H. Arvanaghi, Air temperature estimation using artificial intelligent methods (Case Study : Maragheh City). Eur. J. Sci. Res. 61(2), 290–298 (2011)Google Scholar
  22. 22.
    S.C. Chukwu, A.N. Nwachukwu, Analysis of some meteorological parameters using artificial neural network method for Makurdi, Nigeria. Afr. J. Environ. Sci. Technol. 6(3), 182–188 (2012)CrossRefGoogle Scholar
  23. 23.
    G. Sabri, K.M. Tarek, Combination of artificial neural network models for air quality predictions for the region of Annaba, Algeria. Int. J. Environ. Stud. 69(1), 79–89 (2012)CrossRefGoogle Scholar
  24. 24.
    T. Khatib, A. Mohamed, M. Mahmoud, K. Sopian, A new approach for meteorological variables prediction at Kuala Lumpur, Malaysia, using artificial neural networks: application for sizing and maintaining photovoltaic systems. J. Sol. Energy Eng. 134(2), 021005–021010 (2012)CrossRefGoogle Scholar
  25. 25.
    S. Kirty, Artificial neural network and genetic programming models for meteorological parameter prediction. M.Tech Thesis, Indian Institute of Technology, Bombay, 2008Google Scholar
  26. 26.
    D. Hamby, A review of techniques for parameter sensitivity analysis of environmental models. Environ. Monit. Assess. 32(2), 135–154 (1994)CrossRefGoogle Scholar

Copyright information

© The Institution of Engineers (India) 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceJamia Millia Islamia (Central Univesity)New DelhiIndia
  2. 2.Department of Civil EngineeringIndian Institute of Technology BombayMumbaiIndia

Personalised recommendations