Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan
ABSTRACT “Panta Rhei–Everything Flows” is the science plan for the International
Association of Hydrological Sciences scientific decade 2013–2023. It is founded on the …
Association of Hydrological Sciences scientific decade 2013–2023. It is founded on the …
Artificial neural networks vis-à-vis MODFLOW in the simulation of groundwater: A review
N Zeydalinejad - Modeling Earth Systems and Environment, 2022 - Springer
Although numerical and non-numerical models of groundwater flow and transport have
separately been reviewed in several studies, they have not hitherto been reviewed …
separately been reviewed in several studies, they have not hitherto been reviewed …
[HTML][HTML] Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling
S Razavi - Environmental Modelling & Software, 2021 - Elsevier
Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL),
have created tremendous excitement and opportunities in the earth and environmental …
have created tremendous excitement and opportunities in the earth and environmental …
Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US
Incomplete representations of physical processes often lead to structural errors in process-
based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow …
based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow …
[HTML][HTML] Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism
L Girihagama, M Naveed Khaliq, P Lamontagne… - Neural Computing and …, 2022 - Springer
This study investigates the capability of sequence-to-sequence machine learning (ML)
architectures in an effort to develop streamflow forecasting tools for Canadian watersheds …
architectures in an effort to develop streamflow forecasting tools for Canadian watersheds …
Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective
Abstract Machine learning (ML) applications in Earth and environmental sciences (EES)
have gained incredible momentum in recent years. However, these ML applications have …
have gained incredible momentum in recent years. However, these ML applications have …
Artificial neural network based hybrid modeling approach for flood inundation modeling
Flood inundation models are important tools in flood management. Commonly used flood
inundation models, such as hydrodynamic or simplified conceptual models, are either …
inundation models, such as hydrodynamic or simplified conceptual models, are either …
[HTML][HTML] A stochastic conceptual-data-driven approach for improved hydrological simulations
In a companion paper, Sikorska-Senoner and Quilty (2021) introduced the ensemble-based
conceptual-data-driven approach (CDDA) for improving hydrological simulations. This …
conceptual-data-driven approach (CDDA) for improving hydrological simulations. This …
Exploring the spatio-temporal interrelation between groundwater and surface water by using the self-organizing maps
In this study, we propose a soft-computing methodology to visibly explore the spatio-
temporal groundwater variations of the Kuoping River basin in southern Taiwan. The self …
temporal groundwater variations of the Kuoping River basin in southern Taiwan. The self …
Hydrological modeling of freshwater discharge into Hudson Bay using HYPE
TA Stadnyk, MK MacDonald, A Tefs, SJ Déry… - Elem Sci …, 2020 - online.ucpress.edu
This study details the enhancement and calibration of the Arctic implementation of the
HYdrological Predictions for the Environment (HYPE) hydrological model established for the …
HYdrological Predictions for the Environment (HYPE) hydrological model established for the …