Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique |
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| Authors: | Nastaran Chitsaz Ali Azarnivand Shahab Araghinejad |
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| Institution: | 1. Department of Irrigation and Drainage Engineering, University of Tehran, Tehran, IranNastaranchitsaz@ut.ac.ir;3. Department of Irrigation and Reclamation Engineering, University of Tehran, Karaj, Iran |
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| Abstract: | ABSTRACTAn appropriate streamflow forecasting method is a prerequisite for implementation of efficient water resources management in the water-limited, arid regions that occupy much of Iran. In the current research, monthly streamflow forecasting was combined with three data-driven methods based on large input datasets involving 11 precipitation stations, a natural streamflow, and four climate indices through a long period. The major challenges of rainfall–runoff modelling are generally attributed to complex interacting processes, the large number of variables, and strong nonlinearity. The sensitivity of data-driven methods to the dimension of input/output datasets would be another challenge, so large datasets should be compressed into independently standardized principal components. In this study, three pre-processing techniques were applied: singular value decomposition (SVD) provided more efficient forecasts in comparison to principal component analysis (PCA) and average values of inputs in all networks. Among the data-driven methods, the multi-layer perceptron (MLP) with 1-month lag-time outperformed radial basis and fuzzy-based networks. In general, an increase in monthly lag-time of streamflow forecasting resulted in a decline in forecasting accuracy. The results reveal that SVD was highly effective in pre-processing of data-driven evaluations. |
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| Keywords: | river flow forecasting data-driven models pre-processing singular value decomposition (SVD) large-scale atmospheric circulation |
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