EXTREME STREAMFLOW FORECASTING USING ARTIFICIAL NEURAL NETWORKS

  • H. Y. Maheta
  • H. D. Rank
  • Jaydip J. Makwana
  • G. V. Prajapati
Keywords: Artificial Neural Networks, Rainfall-runoff, Semi-arid Climate

Abstract

Forecasting of extreme stream flow is necessary for water resource planning and management at catchment scale. Artificial neural networks(ANN) have been widely used as models for a variety of nonlinear hydrologic processes including that of forecasting runoff over a watershed. In this study, ANN a data driven technique is used for forecasting the extreme streamflow. ANN architecture is optimized by selection of transfer function, training algorithm, hidden neurons, and initial weights. For ANN weights finalization LM algorithm is used. The performance of ANN model is validated using two different performance indices. It was found that the ANN model consistently gives superior predictions without any explicit consideration of different components of the hydrologic cycle during calibration and validation. Based on the results, ANN modeling appears to be a promising technique for forecasting the extreme streamflow in semiarid Saurashtra regions of Gujarat.

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Author Biographies

H. Y. Maheta

P.G. Institute of Agri-Business Management, Junagadh Agricultural University, Junagadh, INDIA

H. D. Rank

Departments of Soil and Water Conservation Engineering, College of Agricultural Engineering & Technology, Junagadh Agricultural University, Junagadh, INDIA

Jaydip J. Makwana

Centre of Excellence on Soil & Water Management, RTTC, Junagadh Agricultural University, Junagadh, INDIA

G. V. Prajapati

Departments of Soil and Water Conservation Engineering, College of Agricultural Engineering & Technology, Junagadh Agricultural University, Junagadh, INDIA

Published
2018-03-21
How to Cite
Maheta, H., Rank, H., Makwana, J., & Prajapati, G. (2018, March 21). EXTREME STREAMFLOW FORECASTING USING ARTIFICIAL NEURAL NETWORKS. Innovative Farming, 3(1), 31-35. Retrieved from http://innovativefarming.in/index.php/innofarm/article/view/250
Section
Articles

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