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http://dx.doi.org/10.3741/JKWRA.2020.53.6.395

Implementation on the evolutionary machine learning approaches for streamflow forecasting: case study in the Seybous River, Algeria  

Zakhrouf, Mousaab (URMER Laboratory, Faculty of Technology, Department of Hydraulics, University of Tlemcen)
Bouchelkia, Hamid (URMER Laboratory, Faculty of Technology, Department of Hydraulics, University of Tlemcen)
Stamboul, Madani (Research Laboratory of Water Resources, Soil and Environment, Faculty of Architecture and Civil Engineering, Department of Civil Engineering, Amar Telidji University)
Kim, Sungwon (Department of Railroad Construction and Safety Engineering, Dongyang University)
Singh, Vijay P. (Water Engineering, Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A&M University)
Publication Information
Journal of Korea Water Resources Association / v.53, no.6, 2020 , pp. 395-408 More about this Journal
Abstract
This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.
Keywords
Multi-step-days streamflow forecasting; Artificial neural networks; Adaptive neuro-fuzzy inference systems; Wavelet-based neural networks; Genetic algorithm; K-fold cross validation;
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