Related references
Note: Only part of the references are listed.An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction
Zaher Mundher Yaseen et al.
JOURNAL OF HYDROLOGY (2019)
A variable selection package driving Netica with Python
Tomas Beuzen et al.
ENVIRONMENTAL MODELLING & SOFTWARE (2019)
SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact
Nemesio Rodriguez-Fernandez et al.
REMOTE SENSING (2019)
On extreme learning machines in sequential and time series prediction: A non-iterative and approximate training algorithm for recurrent neural networks
Yara Rizk et al.
NEUROCOMPUTING (2019)
A Machine Learning Metasystem for Robust Probabilistic Nonlinear Regression-Based Forecasting of Seasonal Water Availability in the US West
Sean W. Fleming et al.
IEEE ACCESS (2019)
Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland
Alessandro Amaranto et al.
JOURNAL OF HYDROINFORMATICS (2018)
Surface Water Quality Model: Impacts of Influential Variables
Peyman Yousefi et al.
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT (2018)
Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes
Alex J. Cannon
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2018)
Surface Water Quality Model: Impacts of Influential Variables
Peyman Yousefi et al.
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT (2018)
Robustness of Extreme Learning Machine in the prediction of hydrological flow series
Md Atiquzzaman et al.
COMPUTERS & GEOSCIENCES (2018)
Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks
Frederik Kratzert et al.
HYDROLOGY AND EARTH SYSTEM SCIENCES (2018)
Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models
Andrew M. Snauffer et al.
CRYOSPHERE (2018)
Variable complexity online sequential extreme learning machine, with applications to streamflow prediction
Aranildo R. Lima et al.
JOURNAL OF HYDROLOGY (2017)
FORCE learning in recurrent neural networks as data assimilation
Gregory S. Duane
CHAOS (2017)
Potential application of wavelet neural network ensemble to forecast streamflow for flood management
K. S. Kasiviswanathan et al.
JOURNAL OF HYDROLOGY (2016)
Forecasting daily streamflow using online sequential extreme learning machines
Aranildo R. Lima et al.
JOURNAL OF HYDROLOGY (2016)
Bootstrap rank-ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling
John Quilty et al.
WATER RESOURCES RESEARCH (2016)
Accounting for three sources of uncertainty in ensemble hydrological forecasting
Antoine Thiboult et al.
HYDROLOGY AND EARTH SYSTEM SCIENCES (2016)
Improving partial mutual information-based input variable selection by consideration of boundary issues associated with bandwidth estimation
Xuyuan Li et al.
ENVIRONMENTAL MODELLING & SOFTWARE (2015)
On the difficulty to optimally implement the Ensemble Kalman filter: An experiment based on many hydrological models and catchments
A. Thiboult et al.
JOURNAL OF HYDROLOGY (2015)
Data assimilation of ocean wind waves using Neural Networks. A case study for the German Bight
Kathrin Wahle et al.
OCEAN MODELLING (2015)
DEVELOPMENT AND OPERATIONAL TESTING OF A SUPER-ENSEMBLE ARTIFICIAL INTELLIGENCE FLOOD-FORECAST MODEL FOR A PACIFIC NORTHWEST RIVER
Sean W. Fleming et al.
JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION (2015)
Data Assimilation Procedure by Recurrent Neural Network
Fabrício P. Härter et al.
Engineering Applications of Computational Fluid Mechanics (2014)
An evaluation framework for input variable selection algorithms for environmental data-driven models
Stefano Galelli et al.
ENVIRONMENTAL MODELLING & SOFTWARE (2014)
Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information
Fi-John Chang et al.
JOURNAL OF HYDROLOGY (2014)
A strategy for diagnosing and interpreting hydrological model nonstationarity
Seth Westra et al.
WATER RESOURCES RESEARCH (2014)
Low complexity adaptive forgetting factor for online sequential extreme learning machine (OS-ELM) for application to nonstationary system estimations
Jun-seok Lim et al.
NEURAL COMPUTING & APPLICATIONS (2013)
Direct-coupling information measure from nonuniform embedding
D. Kugiumtzis
PHYSICAL REVIEW E (2013)
Extreme Learning Machine for Regression and Multiclass Classification
Guang-Bin Huang et al.
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS (2012)
Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting
Robert J. Abrahart et al.
PROGRESS IN PHYSICAL GEOGRAPHY-EARTH AND ENVIRONMENT (2012)
Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities
Y. Liu et al.
HYDROLOGY AND EARTH SYSTEM SCIENCES (2012)
An experiment on the evolution of an ensemble of neural networks for streamflow forecasting
M. -A. Boucher et al.
HYDROLOGY AND EARTH SYSTEM SCIENCES (2010)
Input variable selection for water resources systems using a modified minimum redundancy maximum relevance (mMRMR) algorithm
Mohamad I. Hejazi et al.
ADVANCES IN WATER RESOURCES (2009)
New approach to applying neural network in nonlinear dynamic model
Fabricio P. Haerter et al.
APPLIED MATHEMATICAL MODELLING (2008)
Comments on The Extreme Learning Machine
Lipo P. Wang et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2008)
Partial mutual information for coupling analysis of multivariate time series
Stefan Frenzel et al.
PHYSICAL REVIEW LETTERS (2007)
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting et al.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2007)
Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology
JW Kirchner
WATER RESOURCES RESEARCH (2006)
Improvement of a parsimonious model for streamflow simulation
C Perrin et al.
JOURNAL OF HYDROLOGY (2003)
A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations
F Aires et al.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES (2001)
Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 - A strategy for system predictor identification
A Sharma
JOURNAL OF HYDROLOGY (2000)
On sequential Monte Carlo sampling methods for Bayesian filtering
A Doucet et al.
STATISTICS AND COMPUTING (2000)
Share Paper
