Optimizing signal decomposition techniques in artificial neural network-based rainfall-runoff model

ABSTRACT

The proper function of artificial neural networks (ANNs) depends on several factors including the suitability of input variables and the amount of information they can add to the model in order to produce the required target output(s). Wavelet transforms and to lesser extent singular spectrum analysis (SSA) are well known and widely applied pre-processing methods to enhance ANN models. An important step in the SSA algorithm and wavelet transform method is choosing the window length (L) and determining the suitable number of decomposition stages, respectively. In most past research, these parameters have been used as granted. Moreover, a research to show the impact of using a combination of wavelet and SSA is absent. This study addresses an approach to optimize window length for SSA and number of decomposition stages for wavelet transform applied in a rainfall-runoff model. Moreover, a hybrid neural network is developed to take the advantage of wavelet and SSA-based ANN models. The results show a significant improvement in model outputs both for optimizing the decomposition parameters and for using the proposed hybrid model.

KEYWORDS:

• Rainfall-runoff model
• singular spectrum analysis
• wavelet
• artificial neural network
• hybrid model

Disclosure statement

No potential conflict of interest was reported by the authors.