Masked Training for Realtime Resilient Streamflow Forecasting: A Spatial-Temporal ML Approach for Real-World Operational Settings - Experimental Dataset

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Authors: Nhat Hoang, Ahmad AlBarqawi, Viravid Na Nagara, Ahmad Ibrahim, Abdallah A. Khreishah, Michel C. Boufadel
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Abstract

This is the experimental dataset used in the paper "Masked Training for Realtime Resilient Streamflow Forecasting: A Spatial-Temporal ML Approach for Real-World Operational Settings", serving the purpose of open-science compliance and facilitate reproducibility in hydrology research. The dataset comprise of: (i). raw discharge data acquired from USGS Water Service API named "usgs_discharge.csv", (ii). the processed discharge data from the raw file as described in the paper named "processed_discharge.csv", (iii). the raw basin characteristics data acquired using StreamStats API named "basin_characteristics.csv", and (iv). the processed basin characteristics data named "processed_basin_chars.csv".

Fields of Study & Subjects

Hydrological Forecasting Using AI Hydrology and Watershed Management Studies Traffic Prediction and Management Techniques

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
24 February 2026
Language
EN
Document type
Dataset