State-of-the-art and comparative review of adaptive sampling methods for kriging
Metamodels aim to approximate characteristics of functions or systems from the knowledge
extracted on only a finite number of samples. In recent years kriging has emerged as a …
extracted on only a finite number of samples. In recent years kriging has emerged as a …
A review of the use of artificial neural network models for energy and reliability prediction. A study of the solar PV, hydraulic and wind energy sources
J Ferrero Bermejo, JF Gómez Fernández… - Applied Sciences, 2019 - mdpi.com
The generation of energy from renewable sources is subjected to very dynamic changes in
environmental parameters and asset operating conditions. This is a very relevant issue to be …
environmental parameters and asset operating conditions. This is a very relevant issue to be …
Deep learning convolutional neural network in rainfall–runoff modelling
SP Van, HM Le, DV Thanh, TD Dang… - Journal of …, 2020 - iwaponline.com
Rainfall–runoff modelling is complicated due to numerous complex interactions and
feedback in the water cycle among precipitation and evapotranspiration processes, and also …
feedback in the water cycle among precipitation and evapotranspiration processes, and also …
Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models
R Arsenault, JL Martel, F Brunet… - Hydrology and Earth …, 2023 - hess.copernicus.org
This study investigates the ability of long short-term memory (LSTM) neural networks to
perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological …
perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological …
Statistical downscaling of precipitation using machine learning techniques
Statistical models were developed for downscaling reanalysis data to monthly precipitation
at 48 observation stations scattered across the Australian State of Victoria belonging to wet …
at 48 observation stations scattered across the Australian State of Victoria belonging to wet …
Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir
M Valipour, ME Banihabib, SMR Behbahani - Journal of hydrology, 2013 - Elsevier
The goal of the present research is forecasting the inflow of Dez dam reservoir by using Auto
Regressive Moving Average (ARMA) and Auto Regressive Integrated Moving Average …
Regressive Moving Average (ARMA) and Auto Regressive Integrated Moving Average …
Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting
G Zuo, J Luo, N Wang, Y Lian, X He - Journal of Hydrology, 2020 - Elsevier
Reliable and accurate streamflow forecasting is vital for water resource management. Many
streamflow prediction studies have demonstrated the excellent prediction ability of …
streamflow prediction studies have demonstrated the excellent prediction ability of …
Generating ensemble streamflow forecasts: A review of methods and approaches over the past 40 years
Ensemble forecasting applied to the field of hydrology is currently an established area of
research embracing a broad spectrum of operational situations. This work catalogs the …
research embracing a broad spectrum of operational situations. This work catalogs the …
Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for
prediction and forecasting in water resources and environmental engineering. However …
prediction and forecasting in water resources and environmental engineering. However …
Data-driven input variable selection for rainfall–runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines
R Taormina, KW Chau - Journal of hydrology, 2015 - Elsevier
Selecting an adequate set of inputs is a critical step for successful data-driven streamflow
prediction. In this study, we present a novel approach for Input Variable Selection (IVS) that …
prediction. In this study, we present a novel approach for Input Variable Selection (IVS) that …