Evaluating the long short-term memory (LSTM) network for discharge prediction under changing climate conditions | Citedby Results | Hydrology Research | IWA Publishing

Skip to Main Content

Skip Nav Destination

Evaluating the long short-term memory (LSTM) network for discharge prediction under changing climate conditions

Hydrology Research (2022) 53 (5): 657–667.

This article has been cited by the following articles in journals that are participating in CrossRef Cited-by Linking.

Assessing rainfall-runoff models for climate change: simple and differential split-sample tests for conceptual and artificial intelligence models

Nazanin Behfar
Martijn J. Booij
Vahid Nourani

Hydrological Sciences Journal (2024) 69 (7): 861.

Uncertainty in Environmental Micropollutant Modeling

Heidi Ahkola
Niina Kotamäki
Eero Siivola
Jussi Tiira
Stefano Imoscopi
Matteo Riva
Ulas Tezel
Janne Juntunen

Environmental Management (2024) 74 (2): 380.

Metamorphic testing of machine learning and conceptual hydrologic models

Peter Reichert
Kai Ma
Marvin Höge
Fabrizio Fenicia
Marco Baity-Jesi
Dapeng Feng
Chaopeng Shen

Hydrology and Earth System Sciences (2024) 28 (11): 2505.

Prediction of hourly inflow for reservoirs at mountain catchments using residual error data and multiple-ahead correction technique

Wen-Dar Guo
Wei-Bo Chen
Chih-Hsin Chang

Hydrology Research (2023) 54 (9): 1072.

Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop

Antara Dasgupta
Louise Arnal
Rebecca Emerton
Shaun Harrigan
Gwyneth Matthews
Ameer Muhammad
Karen O'Regan
Teresa Pérez‐Ciria
Emixi Valdez
Bart van Osnabrugge
Micha Werner
Carlo Buontempo
Hannah Cloke
Florian Pappenberger
Ilias G. Pechlivanidis
Christel Prudhomme
Maria‐Helena Ramos
Peter Salamon

Journal of Flood Risk Management (2023)

Large-scale seasonal forecasts of river discharge by coupling local and global datasets with a stacked neural network: Case for the Loire River system

M.T. Vu
A. Jardani
M. Krimissa
F. Zaoui
N. Massei

Science of The Total Environment (2023) 897: 165494.