Identifying the Sensitivity of Ensemble Streamflow Prediction by Artificial Intelligence
暂无分享,去创建一个
Yen-Ming Chiang | Ruo-Nan Hao | Wen-Ping Tsai | Ying Lin | Y. Chiang | W. Tsai | Ruo‐Nan Hao | Jian-Quan Zhang | Jian-Quan Zhang | Ying-Tien Lin | Ruonan Hao
[1] Edward E. Leamer,et al. Specification Searches: Ad Hoc Inference with Nonexperimental Data , 1980 .
[2] Vijay P. Singh,et al. A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP) , 2016 .
[3] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[4] David H. Wolpert,et al. Stacked generalization , 1992, Neural Networks.
[5] Taha B. M. J. Ouarda,et al. Comparison of ice-affected streamflow estimates computed using artificial neural networks and multiple regression techniques , 2008 .
[6] Shahab Araghinejad,et al. Application of artificial neural network ensembles in probabilistic hydrological forecasting , 2011 .
[7] K. Pearson. VII. Note on regression and inheritance in the case of two parents , 1895, Proceedings of the Royal Society of London.
[8] X. Yin,et al. A reservoir operating method for riverine ecosystem protection, reservoir sedimentation control and water supply , 2014 .
[9] Yen-Ming Chiang,et al. Evaluating the contribution of multi-model combination to streamflow hindcasting by empirical and conceptual models , 2017 .
[10] F. Chang,et al. Exploring the Mechanism of Surface and Ground Water through Data-Driven Techniques with Sensitivity Analysis for Water Resources Management , 2016, Water Resources Management.
[11] Lihua Xiong,et al. Indices for assessing the prediction bounds of hydrological models and application by generalised likelihood uncertainty estimation / Indices pour évaluer les bornes de prévision de modèles hydrologiques et mise en œuvre pour une estimation d'incertitude par vraisemblance généralisée , 2009 .
[12] Ahmed El-Shafie,et al. Optimized Neural Network Prediction Model for Potential Evapotranspiration Utilizing Ensemble Procedure , 2014, Water Resources Management.
[13] Leo Breiman,et al. Stacked regressions , 2004, Machine Learning.
[14] T. Ouarda,et al. Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space , 2007 .
[15] Huan Wang,et al. A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida , 2015, Water Resources Management.
[16] C. M. DeChant,et al. Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation , 2011 .
[17] Y. Yoshida,et al. Downscaling medium-range ensemble forecasts using a neural network approach , 2015 .
[18] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[19] Fi-John Chang,et al. A nonlinear spatio-temporal lumping of radar rainfall for modeling multi-step-ahead inflow forecasts by data-driven techniques , 2016 .
[20] Bithin Datta,et al. Stochastic and Robust Multi-Objective Optimal Management of Pumping from Coastal Aquifers Under Parameter Uncertainty , 2014, Water Resources Management.
[21] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[22] Vinay Kumar Deolia,et al. Neural Network Based Sliding Mode Control for Uncertain Discrete-Time Nonlinear Systems with Time-Varying Delay , 2016 .
[23] J. Adamowski,et al. Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models , 2015, Hydrogeology Journal.
[24] Jery R. Stedinger,et al. Reservoir optimization using sampling SDP with ensemble streamflow prediction (ESP) forecasts , 2001 .
[25] Chandranath Chatterjee,et al. Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs) , 2010 .
[26] Harris Drucker,et al. Improving Regressors using Boosting Techniques , 1997, ICML.
[27] K. Abbaspour,et al. Estimating Uncertain Flow and Transport Parameters Using a Sequential Uncertainty Fitting Procedure , 2004 .
[28] Saumen Maiti,et al. A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction , 2014, Environmental Earth Sciences.
[29] K. Sudheer,et al. Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations , 2013 .
[30] Jan Seibert,et al. On the need for benchmarks in hydrological modelling , 2001 .
[31] Vinay Kumar Deolia,et al. Stabilization of Unknown Nonlinear Discrete-Time Delay Systems Based on Neural Network , 2012 .
[32] Edwin E. Herricks,et al. AI techniques for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands , 2015 .
[33] C. Shu,et al. Regional low‐flow frequency analysis using single and ensemble artificial neural networks , 2009 .
[34] Adrian E. Raftery,et al. Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .
[35] Jan Adamowski,et al. Urban water demand forecasting and uncertainty assessment using ensemble wavelet‐bootstrap‐neural network models , 2013 .