Journal Article10.1177/0309133312444943
Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting
Robert J. Abrahart,François Anctil,Paulin Coulibaly,Christian W. Dawson,Nick J. Mount,Linda See,Asaad Y. Shamseldin,Dimitri Solomatine,Elena Toth,Robert L. Wilby +9 more
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TL;DR: The field is now firmly established and the research community involved has much to offer hydrological science, but it will be necessary to converge on more objective and consistent protocols for selecting and treating inputs prior to model development; extracting physically meaningful insights from each proposed solution; and improving transparency in the benchmarking and reporting of experimental case studies.
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Abstract: This paper traces two decades of neural network rainfall-runoff and streamflow modelling, collectively termed ‘river forecasting’. The field is now firmly established and the research community inv...
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Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks
TL;DR: In this paper, a novel data-driven approach, using the Long Short-Term Memory (LSTM) network, a special type of recurrent neural network, was proposed for modeling storage effects in e.g. catchments with snow influence.
“Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022
Alberto Montanari,G. Young,Hubert H. G. Savenije,Denis A. Hughes,Thorsten Wagener,Lei Ren,Demetris Koutsoyiannis,Christophe Cudennec,Elena Toth,Salvatore Grimaldi,Günter Blöschl,Murugesu Sivapalan,Keith Beven,Keith Beven,Hoshin V. Gupta,Matthew R. Hipsey,Bettina Schaefli,Bettina Schaefli,Berit Arheimer,Eva Boegh,Stanislaus J. Schymanski,G. Di Baldassarre,Bofu Yu,Pierre Hubert,Y. Huang,Andreas Schumann,David A. Post,Veena Srinivasan,Ciaran J. Harman,Sally E. Thompson,Magdalena Rogger,Alberto Viglione,Hilary McMillan,Gregory W. Characklis,Zhonghe Pang,V. Belyaev,V. Belyaev +36 more
TL;DR: The Panta Rhei Everything Flows project as mentioned in this paper is dedicated to research activities on change in hydrology and society, which aims to reach an improved interpretation of the processes governing the water cycle by focusing on their changing dynamics in connection with rapidly changing human systems.
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review
TL;DR: The present review focuses on defining hybrid modeling, the advantages of such combined models, as well as the history and potential future of their application in hydrology to predict important processes of the hydrologic cycle.
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A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources
TL;DR: This work popularizes RF and their variants for the practicing water scientist, and discusses related concepts and techniques, which have received less attention from the water science and hydrologic communities.
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Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines
Riccardo Taormina,Kwok Wing Chau +1 more
TL;DR: A novel approach that employs Binary-coded discrete Fully Informed Particle Swarm optimization and Extreme Learning Machines (ELM) to develop fast and accurate IVS algorithms that are particularly suited for rainfall–runoff modeling applications characterized by high nonlinearity in the catchment dynamics.
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