Advancing Machine Learning-Based Streamflow Prediction Through Event Greedy Selection, Asymmetric Loss Function, and Rainfall Forecasting Uncertainty

Soheyla Tofighi; Faruk Gurbuz; Ricardo Mantilla; Shaoping Xiao

doi:10.3390/app152111656

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Advancing Machine Learning-Based Streamflow Prediction Through Event Greedy Selection, Asymmetric Loss Function, and Rainfall Forecasting Uncertainty

Journal article

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Advancing Machine Learning-Based Streamflow Prediction Through Event Greedy Selection, Asymmetric Loss Function, and Rainfall Forecasting Uncertainty

Soheyla Tofighi, Faruk Gurbuz, Ricardo Mantilla and Shaoping Xiao

Applied sciences, Vol.15(21), 11656

10/31/2025

DOI: 10.3390/app152111656

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Abstract

This paper advances machine learning (ML)-based streamflow prediction by strategically selecting rainfall events, introducing a new loss function, and addressing rainfall forecast uncertainties. Focusing on the Iowa River Basin, we applied the stochastic storm transposition (SST) method to create realistic rainfall events, which were input into a hydrological model to generate corresponding streamflow data for training and testing deterministic and probabilistic ML models. Long short-term memory (LSTM) networks were employed to predict streamflow up to 12 h ahead. An active learning approach was used to identify the most informative rainfall events, reducing data generation effort. Additionally, we introduced a novel asymmetric peak loss function to improve peak streamflow prediction accuracy. Incorporating rainfall forecast uncertainties, our probabilistic LSTM model provided uncertainty quantification for streamflow predictions. Performance evaluation using different metrics improved the accuracy and reliability of our models. These contributions enhance flood forecasting and decision-making while significantly reducing computational time and costs.

hydrology

machine learning

greedy selection

asymmetric peak loss function

uncertainty quantification

LSTM

probabilistic predictive model

Details

Title: Subtitle: Advancing Machine Learning-Based Streamflow Prediction Through Event Greedy Selection, Asymmetric Loss Function, and Rainfall Forecasting Uncertainty
Creators: Soheyla Tofighi - University of Iowa
Faruk Gurbuz
Ricardo Mantilla - University of Manitoba
Shaoping Xiao - University of Iowa
Resource Type: Journal article
Publication Details: Applied sciences, Vol.15(21), 11656
DOI: 10.3390/app152111656
ISSN: 2076-3417
eISSN: 2076-3417
Publisher: MDPI
Grant note: US Department of Education: ED#P116S210005 National Science Foundation: 2226936
This material is based upon work supported by the National Science Foundation under Grant Number 2226936 and the U.S. Department of Education under Grant Number ED#P116S210005. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation and the U.S. Department of Education.
Language: English
Date published: 10/31/2025
Academic Unit: Iowa Technology Institute; Mechanical Engineering
Record Identifier: 9985024165802771

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