Forecasting Monthly Streamflow of Spring-Summer Runoff Season in Rio Grande Headwaters Basin Using Stochastic Hybrid Modeling Approach

Monthly streamflow forecasting during spring-summer runoff season using snow telemetry (SNOTEL) precipitation and snow water equivalent (SWE) as predictors in the Rio Grande Headwaters Basin in Colorado was investigated. The transfer-function noise (TFN) models with SNOTEL precipitation input were built for monthly streamflow. Then, one-month-ahead forecasts of TFN models for the spring-summer runoff season were modified with SWE using an artificial neural networks (ANN) technique denoted in this study as hybrid TFN+ANN. The results indicated that the hybrid TFN+ANN approach not only demonstrated better generalization capability but also improved one-month-ahead forecast accuracy significantly when compared with single TFN and ANN models. The normalized root mean squared errors (NRMSE) of one-month-ahead forecasts of TFN, ANN, and TFN+ANN approaches for spring-summer runoff season were 0.38, 0.30, and 0.25. These findings accentuate that the presented stochastic hybrid modeling approach is an advantageous...

[1]  Jose D. Salas,et al.  Predicting Streamflows Based on Neural Networks , 1995 .

[2]  M. Karamouz,et al.  Seasonal Streamflow Forecasting Using Snow Budget and El Nino-Southern Oscillation Climate Signals: Application to the Salt River Basin in Arizona , 2004 .

[3]  Berna Yazici,et al.  Comparison of ARIMA, neural networks and hybrid models in time series: tourist arrival forecasting , 2007 .

[4]  Simon Jones,et al.  predicting bed demand in a hospital using neural networks and ARIMA models: a hybrid approach , 2005, ESANN.

[5]  Ashu Jain,et al.  Hybrid neural network models for hydrologic time series forecasting , 2007, Appl. Soft Comput..

[6]  R. Abrahart,et al.  Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments , 2000 .

[7]  Keith W. Hipel,et al.  Forecasting monthly riverflow time series , 1985 .

[8]  Periodic Transfer Function-Noise Model for Forecasting , 2005 .

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

[10]  Wen Wang.  Stochasticity, nonlinearity and forecasting of streamflow processes , 2006 .

[11]  Wenrui Huang,et al.  Forecasting flows in Apalachicola River using neural networks , 2004 .

[12]  Fang-Mei Tseng,et al.  Combining neural network model with seasonal time series ARIMA model , 2002 .

[13]  K. Chau,et al.  Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques , 2009 .

[14]  Robert Abrahart,et al.  Neural Network Modelling: Basic Tools and Broader Issues , 2004 .

[15]  Momcilo Markus,et al.  PRECIPITATION-RUNOFF MODELING USING ARTIFICIAL NEURAL NETWORKS AND CONCEPTUAL MODELS , 2000 .

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

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

[18]  Jean Rousselle,et al.  Improving Forecasts of Nile Flood Using SST Inputs in TFN Model , 2000 .

[19]  Keith W. Hipel,et al.  Forecasting Quarter-Monthly Riverflow , 1985 .

[20]  Walter Vandaele,et al.  Applied Time Series and Box-Jenkins Models , 1983 .