Daily Runoff Forecasting Model Based on ANN and Data Preprocessing Techniques

Water

Yun Wang¹  Shenglian Guo¹  Lihua Xiong¹  Pan Liu¹  Dedi Liu¹ 

[1] State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China; E-Mails:

关键词: daily runoff forecasting;    data preprocessing;    linear perturbation model;    singular spectrum analysis;    artificial neural network;   

DOI  :  10.3390/w7084144

来源: mdpi

【 摘 要 】

There are many models that have been used to simulate the rainfall-runoff relationship. The artificial neural network (ANN) model was selected to investigate an approach of improving daily runoff forecasting accuracy in terms of data preprocessing. Singular spectrum analysis (SSA) as one data preprocessing technique was adopted to deal with the model inputs and the SSA-ANN model was developed. The proposed model was compared with the original ANN model without data preprocessing and a nonlinear perturbation model (NLPM) based on ANN, i.e., the NLPM-ANN model. Eight watersheds were selected for calibrating and testing these models. Comparative study shows that the learning and training ability of ANN models can be improved by SSA and NLPM techniques significantly, and the performance of the SSA-ANN model is much better than the NLPM-ANN model, with high foresting accuracy. The SSA-ANN1 model, which only considers rainfall as model input, was compared with the SSA-ANN2 model, which considers both rainfall and previous runoff as model inputs. It is shown that the Nash-Sutcliffe criterion of the SSA-ANN2 model is much higher than that of the SSA-ANN1 model, which means that the proper selection of previous runoff data as rainfall-runoff model inputs can significantly improve model performance since they usually are highly auto-correlated.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.