TY - JOUR
T1 - Multiscale streamflow forecasting using a new Bayesian Model Average based ensemble multi-wavelet Volterra nonlinear method
AU - Rathinasamy, Maheswaran
AU - Adamowski, Jan
AU - Khosa, Rakesh
N1 - Funding Information:
This research was partially funded by an NSERC Discovery Grant held by Jan Adamowski.
PY - 2013/12/12
Y1 - 2013/12/12
N2 - Over the last five years, wavelet transform based models have begun to be explored for hydrologic forecasting applications. In general, a particular wavelet transform (and a particular set of levels of decomposition) is selected as the 'optimal' wavelet transform to be used for forecasting purposes. However, different wavelets have different strengths in capturing the different characteristics of particular hydrological processes. Therefore, relying on a single model based on a single wavelet often leads to predictions that capture some phenomena at the expenses of others. Ensemble approaches based on the use of multiple different wavelets, in conjunction with a multi model setup, could potentially improve model performances and also allow for uncertainty estimation. In this study, a new multi-wavelet based ensemble method was developed for the wavelet Volterra coupled model. Different wavelets, levels of decomposition, and model setups are used in this new approach to generate an ensemble of forecasts. These ensembles are combined using Bayesian Model Averaging (BMA) to develop more skilful and reliable forecasts. The new BMA based ensemble multi-wavelet Volterra approach was applied for forecasting stream flow at different scales (daily, weekly and monthly) observed at two stations in the USA. The results of this study reveal that the proposed BMA based ensemble multi-wavelet Volterra nonlinear model outperforms the single best wavelet Volterra model, as well as the mean averaged ensemble wavelet Volterra model.
AB - Over the last five years, wavelet transform based models have begun to be explored for hydrologic forecasting applications. In general, a particular wavelet transform (and a particular set of levels of decomposition) is selected as the 'optimal' wavelet transform to be used for forecasting purposes. However, different wavelets have different strengths in capturing the different characteristics of particular hydrological processes. Therefore, relying on a single model based on a single wavelet often leads to predictions that capture some phenomena at the expenses of others. Ensemble approaches based on the use of multiple different wavelets, in conjunction with a multi model setup, could potentially improve model performances and also allow for uncertainty estimation. In this study, a new multi-wavelet based ensemble method was developed for the wavelet Volterra coupled model. Different wavelets, levels of decomposition, and model setups are used in this new approach to generate an ensemble of forecasts. These ensembles are combined using Bayesian Model Averaging (BMA) to develop more skilful and reliable forecasts. The new BMA based ensemble multi-wavelet Volterra approach was applied for forecasting stream flow at different scales (daily, weekly and monthly) observed at two stations in the USA. The results of this study reveal that the proposed BMA based ensemble multi-wavelet Volterra nonlinear model outperforms the single best wavelet Volterra model, as well as the mean averaged ensemble wavelet Volterra model.
KW - Bayesian Model Averaging
KW - Ensemble forecasting
KW - Multiscale streamflow forecasting
KW - Wavelet based nonlinear models
UR - http://www.scopus.com/inward/record.url?scp=84887548611&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887548611&partnerID=8YFLogxK
U2 - 10.1016/j.jhydrol.2013.09.025
DO - 10.1016/j.jhydrol.2013.09.025
M3 - Article
AN - SCOPUS:84887548611
SN - 0022-1694
VL - 507
SP - 186
EP - 200
JO - Journal of Hydrology
JF - Journal of Hydrology
ER -