International Journal of
Water Resources and Environmental Engineering

  • Abbreviation: Int. J. Water Res. Environ. Eng.
  • Language: English
  • ISSN: 2141-6613
  • DOI: 10.5897/IJWREE
  • Start Year: 2009
  • Published Articles: 314

Full Length Research Paper

Applications of soft tools to solve hydrological problems for an integrated Indian catchment

Vidyanand Sayagavi
  • Vidyanand Sayagavi
  • Research Scholar, Datta Meghe College of Engineering, Airoli, Navi Mumbai, India.
  • Google Scholar
Shrikant Charhate
  • Shrikant Charhate
  • Pillai HOC College of Engineering and Technology, Rasayani, India.
  • Google Scholar

  •  Received: 29 April 2017
  •  Accepted: 14 June 2017
  •  Published: 31 July 2017


Emergence of a hydrological forecasting model based on past records is crucial in solution of problem. In water resource and hydrology, to build the estimation model based upon the hydrological records, generally requires traditional time series analysis and modelling. Estimation can be done either by using Artificial Intelligence (AI) techniques, or by some traditional methods. The present work uses two data driven techniques, namely Artificial Neural Network (ANN), and Linear Genetic Programming (LGP) to estimate runoff by mixing the data of four gauging stations and evaluating on one catchment out of total five catchments namely Shivade, Shigaon, Gudhe, Amble and Belwadi catchments in the Krishna basin of India, and further the results are compared. The accuracy of model developed was judged by error measures criteria and by drawing time series and scatters comparative graphs. Three types of models are developed considering different combinations. All these models performed considerably well as seen from their performances. From the results it is found that ANN and LGP techniques performed equally well. However LGP performance is better as compared to ANN; as modelling approaches are examined, using the long-term observations of yearly river flow discharges.


Key words: Hydrological process, artificial neural network, linear genetic programming, forecasting runoff, meteorological parameters.