Development of artificial neural-network-based models for the simulation of spring discharge

  • Authors:

  • M. Mohan Raju;R. K. Srivastava;Dinesh C. S. Bisht;H. C. Sharma;Anil Kumar

  • Affiliations:

  • Department of Irrigation and Drainage Engineering, College of Technology, G. B. Pant University of Agriculture and Technology, Uttarakhand, Pantnagar, India;Department of Irrigation and Drainage Engineering, College of Technology, G. B. Pant University of Agriculture and Technology, Uttarakhand, Pantnagar, India;Department of Applied Sciences and Humanities, ITM University, Gurgaon, India;Department of Irrigation and Drainage Engineering, College of Technology, G. B. Pant University of Agriculture and Technology, Uttarakhand, Pantnagar, India;Department of Irrigation and Drainage Engineering, College of Technology, G. B. Pant University of Agriculture and Technology, Uttarakhand, Pantnagar, India

  • Venue:
  • Advances in Artificial Intelligence
  • Year:
  • 2011

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Abstract

The present study demonstrates the application of artificial neural networks (ANNs) in predicting the weekly spring discharge. The study was based on the weekly spring discharge from a spring located near Ranichauri in Tehri Garhwal district of Uttarakhand, India. Five models were developed for predicting the spring discharge based on a weekly interval using rainfall, evaporation, temperature with a specified lag time. All models were developed both with one and two hidden layers. Each model was developed with many trials by selecting different network architectures and different number of hidden neurons; finally a best predicting model presented against each developed model. The models were trained with three different algorithms, that is, quick-propagation algorithm, batch backpropagation algorithm, and Levenberg-Marquardt algorithm using weekly data from 1999 to 2005. A best model for the simulation was selected from the three presented algorithms using the statistical criteria such as correlation coefficient (R), determination coefficient, orNash Sutcliff's efficiency (DC). Finally, optimized number of neurons were considered for the best model. Training and testing results revealed that the models were predicting the weekly spring discharge satisfactorily. Based on these criteria, ANN-based model results in better agreement for the computation of spring discharge. LMR models were also developed in the study, and they also gave good results, but, when compared with the ANN methodology, ANN resulted in better optimized values.