A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events

[1]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[2]  E. Todini.  Rainfall-runoff modeling — Past, present and future , 1988 .

[3]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[4]  A. Shamseldin.  Application of a neural network technique to rainfall-runoff modelling , 1997 .

[5]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[6]  Dragan Savic,et al.  A Genetic Programming Approach to Rainfall-Runoff Modelling , 1999 .

[7]  Shie-Yui Liong,et al.  Rainfall and runoff forecasting with SSA-SVM approach , 2001 .

[8]  Asaad Y. Shamseldin,et al.  A non-linear neural network technique for updating of river flow forecasts , 2001 .

[9]  Christian W. Dawson,et al.  Hydrological modelling using artificial neural networks , 2001 .

[10]  Lihua Xiong,et al.  Comparison of four updating models for real-time river flow forecasting , 2002 .

[11]  Wensheng Wang,et al.  Wavelet Network Model and Its Application to the Prediction of Hydrology , 2003 .

[12]  M. P. Tripathi,et al.  Identification and Prioritisation of Critical Sub-watersheds for Soil Conservation Management using the SWAT Model , 2003 .

[13]  R. Govindaraju,et al.  Geomorphology-based artificial neural networks (GANNs) for estimation of direct runoff over watersheds , 2003 .

[14]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[15]  Dawei Han,et al.  Identification of Support Vector Machines for Runoff Modelling , 2004 .

[16]  R. Abrahart,et al.  Neural Networks for Hydrological Modeling , 2004 .

[17]  R. Moore,et al.  Forecasting for flood warning , 2005 .

[18]  K. Beven,et al.  Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS) , 2005 .

[19]  J. Garbrecht.  Comparison of Three Alternative ANN Designs for Monthly Rainfall-Runoff Simulation , 2006 .

[20]  I-Fan Chang,et al.  Support vector regression for real-time flood stage forecasting , 2006 .

[21]  R. Ibbitt,et al.  Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model , 2007 .

[22]  Asaad Y. Shamseldin,et al.  A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall—runoff models , 2007 .

[23]  K. W. Chau,et al.  River stage prediction based on a distributed support vector regression , 2008 .

[24]  M. Arabi,et al.  Representation of agricultural conservation practices with SWAT , 2008 .

[25]  Narendra Singh Raghuwanshi,et al.  Flood Forecasting Using ANN, Neuro-Fuzzy, and Neuro-GA Models , 2009 .

[26]  Chin H. Wu,et al.  An efficient and accurate non‐hydrostatic model with embedded Boussinesq‐type like equations for surface wave modeling , 2009 .

[27]  Hossein Tabari,et al.  Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran , 2010, Neural Computing and Applications.

[28]  Vahid Nourani,et al.  Integrated artificial neural network for spatiotemporal modeling of rainfall-runoff-sediment processes. , 2010 .

[29]  Linda See,et al.  A conceptual and neural network model for real-time flood forecasting of the Tiber River in Rome , 2010 .

[30]  Niranjan Pramanik,et al.  Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model , 2010, Comput. Geosci..

[31]  Rajib Maity,et al.  Potential of support vector regression for prediction of monthly streamflow using endogenous property , 2010 .

[32]  R. S. Govindaraju,et al.  Artificial Neural Networks in Hydrology , 2010 .

[33]  C. Tsou,et al.  Catastrophic landslide induced by Typhoon Morakot, Shiaolin, Taiwan , 2011 .

[34]  R. Abrahart,et al.  Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models , 2012 .

[35]  Alfred Stein,et al.  Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting , 2013, International Journal of Environmental Science and Technology.

[36]  M. Clark,et al.  Operational hydrological data assimilation with the recursive ensemble Kalman filter , 2013 .

[37]  Lloyd H.C. Chua,et al.  Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning , 2013 .

[38]  A. Arabi Yazdi,et al.  Flood flow forecasting using ANN, ANFIS and regression models , 2013, Neural Computing and Applications.

[39]  Ali Osman Pektas,et al.  ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient , 2013 .

[40]  J. Joo,et al.  A comparison of two event-based flood models (ReFH-rainfall runoff model and HEC-HMS) at two Korean catchments, Bukil and Jeungpyeong , 2014 .

[41]  Gwo-Fong Lin,et al.  Improving the forecasts of extreme streamflow by support vector regression with the data extracted by self‐organizing map , 2014 .

[42]  Chih-Chieh Young,et al.  Genetic algorithm and fuzzy neural networks combined with the hydrological modeling system for forecasting watershed runoff discharge , 2015, Neural Computing and Applications.

[43]  C. Young,et al.  Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model , 2015 .

[44]  A. Feldman.  Hydrologic modeling system HEC-HMS: technical reference manual , 2016 .

[45]  Najmeh Mahjouri,et al.  Integrating Support Vector Regression and a geomorphologic Artificial Neural Network for daily rainfall-runoff modeling , 2016, Appl. Soft Comput..