Data Assimilation for Streamflow Forecasting Using Extreme Learning Machines and Multilayer Perceptrons

[1]  Zaher Mundher Yaseen,et al.  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 , 2019, Journal of Hydrology.

[2]  R. L. Winkler,et al.  Scoring Rules for Continuous Probability Distributions , 1976 .

[3]  D. Solomatine,et al.  Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland , 2018 .

[4]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[5]  K. P. Sudheer,et al.  Potential application of wavelet neural network ensemble to forecast streamflow for flood management , 2016 .

[6]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[7]  Martin F. Lambert,et al.  A strategy for diagnosing and interpreting hydrological model nonstationarity , 2014 .

[8]  Mohammed Atiquzzaman,et al.  Robustness of Extreme Learning Machine in the prediction of hydrological flow series , 2018, Comput. Geosci..

[9]  Tomas Beuzen,et al.  A variable selection package driving Netica with Python , 2019, Environ. Model. Softw..

[10]  Mariette Awad,et al.  On extreme learning machines in sequential and time series prediction: A non-iterative and approximate training algorithm for recurrent neural networks , 2019, Neurocomputing.

[11]  Andrea Castelletti,et al.  An evaluation framework for input variable selection algorithms for environmental data-driven models , 2014, Environ. Model. Softw..

[12]  Haroldo F. de Campos Velho,et al.  Neural network for performance improvement in atmospheric prediction systems: Data Assimilation , 2016 .

[13]  Karsten Schulz,et al.  Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks , 2018, Hydrology and Earth System Sciences.

[14]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[15]  Haroldo F. de Campos Velho,et al.  New approach to applying neural network in nonlinear dynamic model , 2008 .

[16]  S. Sorooshian,et al.  Shuffled complex evolution approach for effective and efficient global minimization , 1993 .

[17]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[18]  Aranildo R. Lima,et al.  Forecasting daily streamflow using online sequential extreme learning machines , 2016 .

[19]  François Anctil,et al.  On the difficulty to optimally implement the Ensemble Kalman filter: An experiment based on many hydrological models and catchments , 2015 .

[20]  Joanna Staneva,et al.  Data assimilation of ocean wind waves using Neural Networks. A case study for the German Bight , 2015 .

[21]  Sean W. Fleming,et al.  A Machine Learning Metasystem for Robust Probabilistic Nonlinear Regression-Based Forecasting of Seasonal Water Availability in the US West , 2019, IEEE Access.

[22]  Ashish Sharma,et al.  Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification , 2000 .

[23]  Jan Adamowski,et al.  Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling , 2016 .

[24]  F. Anctil,et al.  An experiment on the evolution of an ensemble of neural networks for streamflow forecasting , 2009 .

[25]  Philippe Richaume,et al.  SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact , 2019, Remote. Sens..

[26]  Seokjin Lee,et al.  Low complexity adaptive forgetting factor for online sequential extreme learning machine (OS-ELM) for application to nonstationary system estimations , 2012, Neural Computing and Applications.

[27]  Aranildo R. Lima,et al.  Variable complexity online sequential extreme learning machine, with applications to streamflow prediction , 2017 .

[28]  S. Sorooshian,et al.  Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information , 2014 .

[29]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[30]  Fabrício P. Härter,et al.  Data Assimilation Procedure by Recurrent Neural Network , 2012 .

[31]  Gholamreza Naser,et al.  Surface Water Quality Model: Impacts of Influential Variables , 2018 .

[32]  S. Frenzel,et al.  Partial mutual information for coupling analysis of multivariate time series. , 2007, Physical review letters.

[33]  F. Aires,et al.  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 , 2001 .

[34]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[35]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[36]  P. Coulibaly,et al.  Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting , 2012 .

[37]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[38]  Seong Jin Noh,et al.  Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities , 2012 .

[39]  Dimitris Kugiumtzis,et al.  Direct coupling information measure from non-uniform embedding , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[40]  Dave Campbell,et al.  Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River , 2015 .

[41]  L. P. Wang,et al.  Comments on "The Extreme Learning Machine" , 2008, IEEE Trans. Neural Networks.

[42]  Alex J. Cannon,et al.  Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models , 2017 .

[43]  Ximing Cai,et al.  Input variable selection for water resources systems using a modified minimum redundancy maximum relevance (mMRMR) algorithm , 2009 .

[44]  J. Kirchner.  Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology , 2006 .

[45]  Jeffrey P. Walker,et al.  Extended versus Ensemble Kalman Filtering for Land Data Assimilation , 2002 .

[46]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[47]  Aaron C. Zecchin,et al.  Improving partial mutual information-based input variable selection by consideration of boundary issues associated with bandwidth estimation , 2015, Environ. Model. Softw..

[48]  François Anctil,et al.  Accounting for three sources of uncertainty in ensemble hydrological forecasting , 2015 .

[49]  C. Perrin,et al.  Improvement of a parsimonious model for streamflow simulation , 2003 .

[50]  Alex J. Cannon,et al.  Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes , 2018, Stochastic Environmental Research and Risk Assessment.

[51]  Gregory S Duane,et al.  "FORCE" learning in recurrent neural networks as data assimilation. , 2017, Chaos.