Enhancing Streamflow Simulations with Gated Recurrent Units Deep Learning Models in the Flood Prone Region with Low-Convergence Streamflow Data-Reference-Cited by-同舟云学术

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Enhancing Streamflow Simulations with Gated Recurrent Units Deep Learning Models in the Flood Prone Region with Low-Convergence Streamflow Data

Published:2024-09 Issue: Volume: Page:103737
ISSN:1474-7065
Container-title:Physics and Chemistry of the Earth, Parts A/B/C
language:en
Short-container-title:Physics and Chemistry of the Earth, Parts A/B/C

Author:

Karamvand Aliakbar^ORCID,Hosseini Seyed Abbas^ORCID,Aziz Seyed Ali

Publisher

Elsevier BV

Reference47 articles.

1. A new approach to river flow forecasting: LSTM and GRU multivariate models;Adriano de Melo;IEEE Lat Am Trans,2019

2. Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting;Apaydin;Water,2020

3. Zoning of Flood Risk in Kashkan River basin using Two Models WOE and EBF;Azadi;J Geogr,2020

4. Streamflow forecasting by SVM with quantum behaved particle swarm optimization;Ch;Neurocomputing,2013

5. Cho K, Van Merriënboer B, Gulcehre C, et al (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. EMNLP 2014 - 2014 Conf Empir Methods Nat Lang Process Proc Conf 1724–1734. https://doi.org/10.3115/v1/d14-1179

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