Successive-Station Streamflow Prediction and Precipitation Uncertainty Analysis in the Zarrineh River Basin Using a Machine Learning Technique
نویسندگان
چکیده
Precise forecasting of streamflow is crucial for the proper supervision water resources. The purpose present investigation to predict successive-station using Gated Recurrent Unit (GRU) model and quantify impact input information (i.e., precipitation) uncertainty on GRU model’s prediction Generalized Likelihood Uncertainty Estimation (GLUE) computation. Zarrineh River basin in Lake Urmia, Iran, was nominated as case study due importance location its significant contribution lake inflow. Four stations were considered from upstream downstream. yielded highly accurate all stations. future precipitation data generated under Representative Concentration Pathway (RCP) scenarios used estimate effect prediction. p-factor (inside interval) r-factor (width indices evaluate uncertainty. GLUE predicted reliable ranges 0.47 0.57 61.6% 89.3% p-factor.
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ژورنال
عنوان ژورنال: Water
سال: 2023
ISSN: ['2073-4441']
DOI: https://doi.org/10.3390/w15050999