Comprehensive performance analysis of training functions in flow prediction model using artificial neural network

Authors

  • KC Shikhar Institute of Water Resources and Hydropower, Hohai University, Nanjing, China
  • Khem Prasad Bhattarai Institute for Integrated Hydro-Environment Research, Kathmandu, Nepal
  • Tang De Shan Institute of Water Resources and Hydropower, Hohai University, Nanjing, China
  • Saurabh Mishra Institute of Water Science and Technology, Hohai University, Nanjing, China
  • Ishwar Joshi Institute for Integrated Hydro-Environment Research, Kathmandu, Nepal
  • Anurag Kumar Singh School of Chemical Technology, Guru Govind Singh Indraprastha University, New Delhi, India

DOI:

https://doi.org/10.17159/wsa/2024.v50.i2.4099

Keywords:

artificial neural network, training function, snow-fed catchment, data-driven models, flow prediction

Abstract

Higher Himalayan catchments are often poorly monitored for hydrological activities involving flood flow prediction for the safety of riverside communities and the successful operation of hydropower projects. This study aimed to estimate the comparative performance of artificial neural network (ANN) based flow prediction models using 10 years of daily river flow data of Kaligandaki catchment at Kotagaun, Nepal, which is a snow-fed catchment in the Himalayan region. The flow prediction models were trained and tested at a hydrological station using the previous 3 days’ river flow data to predict the 1-day ahead flow data. Eight different training functions were employed in an ANN model for comprehensive statistical assessment of accuracy and precision of each training function. The most significant and validated result obtained in this study is the comprehensive comparison of various training functions’ performance, and identification of the most efficient training function for the study case. Among the training functions investigated, the Levenberg-Marquardt backpropagation function exhibits the best performance for the model having Nash-Sutcliffe efficiency, root mean square error and mean absolute error values of 0.866, 209.578 and 75.422, respectively. This study provides a fundamental basis for accurate flow prediction of topographically challenged catchments where hydrological monitoring and data collection may be limited. In particular, this model will help to improve early warning system, hydrological planning, and the safety of riverside communities in the Himalayan region.

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Published

2024-04-30

How to Cite

KC Shikhar, Khem Prasad Bhattarai, Tang De Shan, Saurabh Mishra, Ishwar Joshi, & Anurag Kumar Singh. (2024). Comprehensive performance analysis of training functions in flow prediction model using artificial neural network. Water SA, 50(2 April). https://doi.org/10.17159/wsa/2024.v50.i2.4099

Issue

Section

Research paper