Neural Network Models for Prediction of Evaporation Based on Weather Variables

  • RakheeEmail author
  • Archana Singh
  • Amrender Kumar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)


Artificial Neural networks (ANNs) is a computation method that can be utilized for predictions. In this study prediction of evaporation using ANN’s multilayer perceptron (MLP) is attempted considering different weather variables viz. Relative Humidity Morning & Evening, Bright Sunshine Hours, Rainfall, Maximum & Minimum temperature, Mean Temperature and Mean Relative Humidity. The analysis is done over different parts of India viz. Raipur, Pantnagar, Karnal, Hyderabad and Samastipur. Weather of four lag weeks from week of forecast is considered for the model development. The lag periods were also utilized to develop weather indices. Subsequent two years were not included while developing the model for predicting evaporation for different locations. The performance of the developed models was evaluated based on Root Mean Square Error (RMSE).


Artificial Neural Networks Prediction models Weather indices Backpropagation algorithm Mean absolute percentage error 



The work would not have been possible without the data. we thank Agricultural Knowledge Management Unit, ICAR-IARI, New Delhi for providing us with the data from various locations of India.


  1. 1.
    Mathur, S., Kumar, A., Chandra, M.: A feature based neural network model for weather forecasting. World Acad. Sci. Eng. Technol. 34, 66–73 (2007)Google Scholar
  2. 2.
    Hayati, M., Mohebi, Z.: Application of artificial neural networks for temperature forecasting. World Acad. Sci. Eng. Technol. 28, 275–279 (2007)Google Scholar
  3. 3.
    Santhosh Baboo, S., Kadar Shereef, I.: An efficient weather forecasting system using artificial neural network. Int. J. Environ. Sci. Dev. 1(4), 321–326 (2010)CrossRefGoogle Scholar
  4. 4.
    Dewolf, E.D., Francl, L.J.: Neural network that distinguish in period of wheat tan spot in an outdoor environment. Phytopathology 87(1), 83–87 (1997)CrossRefGoogle Scholar
  5. 5.
    Dewolf, E.D., Francl, L.J.: Neural network classification of tan spot and stagonespore blotch infection period in wheat field environment. Phytopathology 20(2), 108–113 (2000)CrossRefGoogle Scholar
  6. 6.
    Madhav, K.V.: Study of statistical modeling techniques in agriculture. Ph.D. thesis, IARI, New Delhi (2003)Google Scholar
  7. 7.
    Kumar, M., Raghuwanshi, N.S., Singh, R., Wallender, W.W., Pruitt, W.O.: Estimating Evapotranspiration using artificial neural network. J. Irrig. Drain. Eng. 128(4), 224–233 (2002)CrossRefGoogle Scholar
  8. 8.
    Agrawal, R., Mehta, S.C.: Weather based forecasting of crop yields, pests and diseases - IASRI models. J. Ind. Soc. Agril. Statist. 61(2), 255–263 (2007)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Chattopadhyay, C., et al.: Forecasting of Lipaphis erysimi on oilseed Brassicas in India - a case study. Crop. Prot. 24, 1042–1053 (2005)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Chattopadhyay, C., et al.: Epidemiology and forecasting of Alternaria blight of oilseed Brassica in India - a case study. Zeitschrift für Pflanzenkrankheiten und Pflanzenschutz 112, 351–365 (2005)Google Scholar
  11. 11.
    Desai, A.G., et al.: Brassica juncea powdery mildew epidemiology and weather-based forecasting models for India - a case study. J. Plant Dis. Prot. 111(5), 429–438 (2004)Google Scholar
  12. 12.
    Klimasauskas, C.: Applying neural networks. Part 3: Training a neural network (1991)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.CSEAmity UniversityNoidaIndia
  2. 2.Agricultural Knowledge Management UnitICAR-IARIDelhiIndia

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