Application of Wavelet Denoising and Artificial Intelligence Models for Stream Flow Forecasting

Document Type: Original Article

Authors

1 Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

2 Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz,Tabriz, Iran.

Abstract

In this study, the ability of threshold based wavelet denoising Least Square Support Vector Machine (LSSVM) and Artificial Neural Network (ANN) models were evaluated for forecasting daily Multi-Station (MS) streamflow of the Snoqualmie watershed. For this aim, at first step, outflow of the watershed was forecasted via ad hoc LSSVM and ANN models just by one station individually. Therefore, MS-LSSVM and MS-ANN were employed to use entire information of all sub-basins synchronously. Finally, the streamflow of sub-basins were denoised via wavelet based thresholding method, then the purified signals were imposed into the LSSVM and ANN models in a MS framework. The results showed the superiority of ANN to the LSSVM, MS model to the individual sub-basin model, using denoised data with regard to the noisy data, e.g., DCLSSVM=0.82, DCANN=0.85, DCMS-ANN=0.91, DCdenoised-MS-ANN=0.94.

Keywords


1. Tongal, H., Booij, M. J., (2018). Simulation and forecasting of streamflows using machine learning models coupled with base flow separation. Journal of Hydrology 564, 266–282.

2. Danandeh Mehr, A., (2018). An improved gene expression programming model for streamflow forecasting in intermittent streams. Journal of Hydrology 563, 669–678.

3. Kalteh, A., (2016). Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques. Water Resources Management 2, 747–766.

4. Prasad, R., Deo, R. C., Li, Y., Maraseni, T., (2017). Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm. Atmospheric Research 197, 42–63.

5. Adnan, R. M., Yuan, X., Kisi, O., Adnan, M., Mehmood, A., (2018). Stream Flow Forecasting of Poorly Gauged Mountainous Watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree Using Climatic Data from Nearby Station. Water Resources Management 14, 4469–4486.

6. Jansen, M., (2006). Minimum Risk Thresholds for Data with Heavy Noise. IEEE Signal Processing Letters 13, 296–299.

7. Guo, J., Zhou, J., Qin, H., Zou, Q., Li, Q., 2011. Monthly Streamflow Forecasting Based on Improved Support Vector Machine Model. Expert Systems with Applications 38, 13073–13081.

8. Nejad, F. H., Nourani, V., (2012). Elevation of Wavelet Denoising Performance via an ANN-Based Streamflow Forecasting Model. International Journal of Computer Science and Management Research 1, 764–770.

9. Nourani, V., Mousavi, S., (2016). Spatiotemporal Groundwater Level Modeling using Hybrid Artificial Intelligence-Meshless Method. Journal of Hydrology 536, 10–25.

10. Nourani, V., Komasi, M. (2013). A Geomorphology-Based ANFIS Model for Multi-Station Modeling of Rainfall–Streamflow Process. Journal of Hydrology 490, 41–55.

11. Lee, W. K., Resdi, T. A. T., (2016). Simultaneous Hydrological Prediction at Multiple Gauging Stations using the NARX Network for Kemaman Catchment, Terengganu, Malaysia. Hydrological Science Journal 61, 2930–2945.

12. Nourani, V., Andalib, G., Sadikoglu, F., Sharghi, E., (2017). Cascade-based multi-scale AI approach for modelling rainfall-runoff process. Hydrology Research 49, 1191-1207.

13. Nourani, V., Andalib, G., (2015). Wavelet Based Artificial Intelligence Approaches for Prediction of Hydrological Time Series. In: Chalup S.K., Blair A. D., Randall M. (eds) Artificial Life and Computational Intelligence. ACALCI 2015. Lecture Notes in Computer Science, vol 8955. Springer, Cham

14. Donoho, D. H., (1995). Denoising by Soft-Thresholding. IEEE Transactions on Information Theory 41, 613–617.

15. Suykens, J. A. K., Vandewalle, J., (1999).  Least Square Support Vector Machine Classifiers. Neural Processing Letters 9, 293–300.

16. Legates, D. R., McCabe, Jr. G. J., (1999). Evaluating the Use of Goodness-of-Fit Measures in Hydrologic and Hydroclimatic Model Validation. Water Resources Research 35, 233–241.