Daily rainfall-runoff forecasting using Bayesian echo state network
Chuntian Cheng, Baojian Li, Sen Wang, Xinyu Wu · Scientia Sinica Technologica · 2014
The echo state network (ESN) is simpler and costs less training time than traditional recurrent neural networks. Due to linear regression algorithm usually adopted by standard ESN to calibrate model parameters, the over-fitting phenomenon easily occurs. To overcome this shortcoming, a Bayesian echo state network (BESN) model is proposed for daily rainfall-runoff forecasting. The BESN model combined Bayesian theory and ESN obtains the optimal output weights via maximizing posterior probabilistic density and improves its generalization ability. Two Case studies on daily inflow forecasting for Ansha Reservoir and Xinfengjiang Reservoir show that the BESN model is effective and feasible and can provide better forecast accuracy than the traditional BP neural network and ESN models.