A novel physics-aware machine learning-based dynamic error correction model for improving streamflow forecast accuracy

Abhinanda Roy, K. S. Kasiviswanathan*, Sandhya Patidar, Adebayo J. Adeloye, Bankaru-Swamy Soundharajan, Chandra S. P. Ojha

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
72 Downloads (Pure)

Abstract

Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near-accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow-fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7-day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time.

Original languageEnglish
Article numbere2022WR033318
JournalWater Resources Research
Volume59
Issue number2
Early online date31 Jan 2023
DOIs
Publication statusPublished - Feb 2023

Keywords

  • Forecasting
  • Machine learning (ML)
  • Flooding
  • Error correction model
  • hybrid model
  • uncertainty quantification
  • HBV model
  • streamflow forecast
  • particle filter
  • random forest

ASJC Scopus subject areas

  • Water Science and Technology

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