Mid- and long-term runoff predictions by an improved phase-space reconstruction model.

[1]  Southern Double Stars , 1877, Nature.

[2]  G. P. Cressman.  AN OPERATIONAL OBJECTIVE ANALYSIS SYSTEM , 1959 .

[3]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[4]  H. Goldstein.  Multilevel mixed linear model analysis using iterative generalized least squares , 1986 .

[5]  Klaus Fraedrich,et al.  Estimating Weather and Climate Predictability on Attractors , 1987 .

[6]  Peter H. Gleick,et al.  The development and testing of a water balance model for climate impact assessment: Modeling the Sacramento Basin , 1987 .

[7]  M. Franchini,et al.  Comparative analysis of several conceptual rainfall-runoff models , 1991 .

[8]  Mark S. Seyfried,et al.  Searching for chaotic dynamics in snowmelt runoff , 1991 .

[9]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[10]  P. Bryant.  Extensional singularity dimensions for strange attractors , 1993 .

[11]  A. Jayawardena,et al.  Analysis and prediction of chaos in rainfall and stream flow time series , 1994 .

[12]  Akira Kawamura,et al.  Prediction of unspots using reconstructed chaotic system equations , 1995 .

[13]  Louis M. Pecora,et al.  Fundamentals of synchronization in chaotic systems, concepts, and applications. , 1997, Chaos.

[14]  Firdaus E. Udwadia,et al.  An efficient QR based method for the computation of Lyapunov exponents , 1997 .

[15]  Slobodan P. Simonovic,et al.  Short term streamflow forecasting using artificial neural networks , 1998 .

[16]  Thian Yew Gan,et al.  Hydroclimatic trends and possible climatic warming in the Canadian Prairies , 1998 .

[17]  Qiang Wang,et al.  Biases of correlation dimension estimates of streamflow data in the Canadian prairies , 1998 .

[18]  N. Arnell.  The effect of climate change on hydrological regimes in Europe: a continental perspective , 1999 .

[19]  A. Jayawardena,et al.  Noise reduction and prediction of hydrometeorological time series: dynamical systems approach vs. stochastic approach , 2000 .

[20]  Akira Kawamura,et al.  Dynamics of monthly rainfall-runoff process at the Gota basin: A search for chaos , 2000 .

[21]  R. Lidén,et al.  Analysis of conceptual rainfall–runoff modelling performance in different climates , 2000 .

[22]  A. Elshorbagy,et al.  Performance Evaluation of Artificial Neural Networks for Runoff Prediction , 2000 .

[23]  Tiesong Hu,et al.  River flow time series prediction with a range-dependent neural network , 2001 .

[24]  Ashu Jain,et al.  Short-Term Water Demand Forecast Modelling at IIT Kanpur Using Artificial Neural Networks , 2001 .

[25]  Ka Chi Lam,et al.  River flow time series prediction with improved neural network , 2001 .

[26]  Magnus Persson,et al.  Monthly runoff prediction using phase space reconstruction , 2001 .

[27]  Bellie Sivakumar,et al.  Characterization and prediction of runoff dynamics: a nonlinear dynamical view , 2002 .

[28]  Bellie Sivakumar,et al.  A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers , 2002 .

[29]  Bellie Sivakumar,et al.  River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches , 2002 .

[30]  F. Udwadia,et al.  Computation of Lyapunov characteristic exponents for continuous dynamical systems , 2002 .

[31]  Alistair Sutcliffe,et al.  Assessing the reliability of socio‐technical systems , 2003 .

[32]  Yen-Ming Chiang,et al.  Comparison of static-feedforward and dynamic-feedback neural networks for rainfall -runoff modeling , 2004 .

[33]  Guobin Fu,et al.  Hydro-Climatic Trends of the Yellow River Basin for the Last 50 Years , 2004 .

[34]  P. C. Nayak,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[35]  Zhou Yan-ping.  Application of Neural Network and GA in Runoff Prediction Model , 2004 .

[36]  Hongxing Zheng,et al.  Changes in components of the hydrological cycle in the Yellow River basin during the second half of the 20th century , 2004 .

[37]  Start Regional.  Intercomparison of Spatial Interpolation Basedon Observed Precipitation Data , 2004 .

[38]  K. P. Sudheer,et al.  Rainfall‐runoff modelling using artificial neural networks: comparison of network types , 2005 .

[39]  洪梅,et al.  NON-LINEAR DYNAMIC MODEL RETRIEVAL OF SUBTROPICAL HIGH BASED ON EMPIRICAL ORTHOGONAL FUNCTION AND GENETIC ALGORITHM , 2006 .

[40]  Mohamed Nasr Allam,et al.  Rainfall‐runoff modelling using artificial neural networks technique: a Blue Nile catchment case study , 2006 .

[41]  Non-linear dynamic model retrieval of subtropical high based on empirical orthogonal function and genetic algorithm , 2006 .

[42]  Jeffrey G. Arnold,et al.  Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .

[43]  Alberto Montanari,et al.  AFFDEF: A spatially distributed grid based rainfall-runoff model for continuous time simulations of river discharge , 2007, Environ. Model. Softw..

[44]  Huang Qiang,et al.  Chaotic local-region multi-step forecasting model of flow time series and its application on the upper reach of the Yellow river , 2007 .

[45]  Soichi Nishiyama,et al.  Neural Networks for Real Time Catchment Flow Modeling and Prediction , 2007 .

[46]  Huang Qiang.  Nearest Adjacent Forecasting Model and Its Application to Stream Flow Prediction , 2008 .

[47]  Nelson F. F. Ebecken,et al.  Parameter Identification of Recurrent Fuzzy Systems With Fuzzy Finite-State Automata Representation , 2008, IEEE Transactions on Fuzzy Systems.

[48]  Hong Mei,et al.  Retrieval Non‐Linear Dynamic Forecast Model Of El Nino/ La Nina Index Based on Genetic Algorithm Optimization , 2008 .

[49]  Lihua Xiong,et al.  An empirical method to improve the prediction limits of the GLUE methodology in rainfall–runoff modeling , 2008 .

[50]  Maziar Palhang,et al.  Generalization performance of support vector machines and neural networks in runoff modeling , 2009, Expert Syst. Appl..

[51]  U. Panu,et al.  Analysis of annual hydrological droughts: the case of northwest Ontario, Canada , 2009 .

[52]  Chuntian Cheng,et al.  A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series , 2009 .

[53]  C. L. Wu,et al.  Methods to improve neural network performance in daily flows prediction , 2009 .

[54]  K. Chau,et al.  A hybrid model coupled with singular spectrum analysis for daily rainfall prediction , 2010 .

[55]  M. Fiorentino,et al.  Best Fit and Selection of Theoretical Flood Frequency Distributions Based on Different Runoff Generation Mechanisms , 2010 .

[56]  Zhao Xiao-shen.  Review on Study of Mid-and Long-term Hydrological Forecasting Technique , 2010 .

[57]  Nelson F. F. Ebecken,et al.  Long-Term Runoff Modeling Using Rainfall Forecasts with Application to the Iguaçu River Basin , 2011 .

[58]  Xiao-Yan Li,et al.  Coastline change of the Yellow River estuary and its response to the sediment and runoff (1976–2005) , 2011 .

[59]  I. Jung,et al.  The temporal and spatial structures of recent and future trends in extreme indices over Korea from a regional climate projection , 2011 .

[60]  G. Hegerl,et al.  Human contribution to more-intense precipitation extremes , 2011, Nature.

[61]  Axel Bronstert,et al.  Evaluation of medium-range runoff forecasts for a 50 km 2 watershed , 2012 .

[62]  Chao‐An Chen,et al.  Mechanisms for Global Warming Impacts on Precipitation Frequency and Intensity , 2012 .

[63]  Evolution modes and EMD gray self-memory model of the Yellow River water resources , 2012 .

[64]  Pejman Tahmasebi,et al.  A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation , 2012, Comput. Geosci..

[65]  Alexandre Evsukoff,et al.  A multi-model approach for long-term runoff modeling using rainfall forecasts , 2012, Expert Syst. Appl..

[66]  Pinar Civicioglu,et al.  Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm , 2012, Comput. Geosci..

[67]  Dong Wang,et al.  Application of Bayesian Model Averaging Approach to Multimodel Ensemble Hydrologic Forecasting , 2013 .

[68]  The Applicability and Prospect of SWAT Model in the Yellow River Valley in Ningxia Hui Autonomous Region , 2013 .

[69]  Florin Pop,et al.  HELGA: a heterogeneous encoding lifelike genetic algorithm for population evolution modeling and simulation , 2014, Soft Comput..

[70]  Zongxue Xu,et al.  Runoff predictions in ungauged catchments in southeast Tibetan Plateau , 2014 .

[71]  Zhao Xue-hu.  Application of Data-driven Model Based on Empirical Mode Decomposition for Runoff Forecasting , 2014 .

[72]  K. Chau,et al.  Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers , 2015 .

[73]  Kwok-wing Chau,et al.  A novel hybrid neural network based on continuity equation and fuzzy pattern-recognition for downstream daily river discharge forecasting , 2015 .

[74]  Kwok-Wing Chau,et al.  ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS , 2015, Eng. Appl. Artif. Intell..

[75]  U. Panu,et al.  Predicting return periods of hydrological droughts using the Pearson 3 distribution: a case from rivers in the Canadian prairies , 2015 .

[76]  Kwok-wing Chau,et al.  Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition , 2015, Water Resources Management.