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Wavelet-based feature extraction of rainfall-runoff process via Self-Organizing Map

One of the essential steps in any modeling is the determination of dominant input variables, else it may lead to poor model accuracy. Rainfall and runoff time series due to various seasonalities involved in the processes may have numerous possible subsets. Therefore, looking for an efficient mathematical-based tool in order to capture dominant subsets seems inevitable. The wavelet transform is a drastic mathematical tool to capture the multi-scale features of a signal where on the other hand the Self-Organizing Map (SOM) can be a powerful technique capable of ordering multivariate data by similarity as preserving the topological structure of the data. The current paper presents a procedure to extract dominant features of rainfall and runoff process obtained from the Delaney Creek Sub-basin located in Tampa Bay Watershed at Florida, USA. Firstly, the wavelet technique was used to decompose the main rainfall and runoff time series into several sub-series. Subsequently, several independent sub-series were chosen via SOM. Results revealed the efficiency of the applied methodology in choosing dominant subsets of the input data. Key-Words: Rainfall-runoff modeling; features extraction; Wavelet transform; Clustering; Self-Organizing Map; Delaney Creek Sub-basin