Abstract
Abstract In this paper, runoff time series of the sub-basins in a cascade form were decomposed by Wavelet Transform (WT) to extract their dynamical and multi-scale features for modeling Multi-Station (MS) rainfall-runoff (R-R) process of the Little River Watershed (LRW) in USA. A Self-Organizing Map (SOM) clustering technique was also employed to find homogeneous extracted sub-series' clusters. As a complementary feature, extraction criterion of mutual information (MI) was utilized for proper cluster agent choice to impose to the artificial intelligence (AI) models (Feed Forward Neural Network, FFNN; Extreme Learning Machine, ELM; and Least Square Support Vector Machine, LSSVM) to predict the runoff of the LRW sub-basins. The performance of wavelet-based runoff prediction was compared to the Markovian-based MS model. The proposed method not only considers the prediction of the outlet runoff but also covers predictions of interior sub-basins behavior. The outcomes showed that the proposed AI-models combined with the SOM and MI tools enhanced the MS runoff prediction efficiency up to 23% in comparison with the Markovian-based models. Nevertheless, benefit of the seasonality of the process along with reduction of dimension of the inputs could help the AI-models to consume pure information of the recorded data.
Highlights
Conversion of rainfall to runoff, according to the laws of gravity, vivifies earth, replenishes groundwater, keeps rivers and lakes full of water, and varies the landscape by the action of erosion
The sub-basins daily rainfall and runoff time series were considered over the study area as the potential inputs and the Little River Watershed (LRW) interior and outlet runoff values were considered as the artificial intelligence (AI) models outputs
Considering the second scenario and to improve the first scenario results, data pre-processing using feature extraction methods of Wavelet Transform (WT) and Self-Organizing Map (SOM)-mutual information (MI) led to important hydrological parameters detection which proved helpful in enhancing AIbased MS runoff predictions
Summary
Conversion of rainfall to runoff, according to the laws of gravity, vivifies earth, replenishes groundwater, keeps rivers and lakes full of water, and varies the landscape by the action of erosion. The benefit of rainfall-runoff (R-R) modeling as a role of science is providing information for engineers and decision makers to manage, protect, and enhance water resources. Various black box methods such as artificial intelligence (AI) models have been already presented for R-R simulation (e.g. Sun et al ; Yaseen et al ; Chadalawada et al ; Kwin et al ; Nourani ). The Wavelet Transform (WT) as a data pre-processor presents better
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
