Modeling Earth Systems and Environment

, Volume 4, Issue 3, pp 1021–1028 | Cite as

Development of streamflow prediction models for a weir using ANN and step-wise regression

  • Muhammad HassanEmail author
  • Haseeb Zaffar
  • Imran Mehmood
  • Anwar Khitab
Original Article


The study was aimed to develop an accurate prediction model for streamflow at Patrind Hydropower Station located on Kunhar river right on the border line of district Abbottabad, KPK and district Muzaffarabad, AJK. Antecedent meteorological data condition of upstream site was considered as inputs. A variety of input combinations were made to select the suitable ones for smooth model development. The model training was carried out using two artificial neural networking techniques; two layer back propagation and Broyden Fletcher GoldfrabShano. The results were also compared with a step-wise regression based model. The best model was evaluated on the basis of distributive statistics analysis such as root mean square error, bias, variance and correlation coefficient R2. All the developed models showed extraordinary results with very high values of model efficiency (more than 95%) but model based upon step-wise regression technique outperformed all the other models with low values of variance and BIAS.


ANN Meteorological conditions Model development Statistics analysis streamflow Training 



The authors would like to acknowledge the guidance and support of Late Sir Muhammad Ali Shamim to carry out this research work. The authors are also thankful to the students of civil engineering department, MUST for their effort and contribution in this project.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringMUSTMirpurPakistan

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