Assessment of Wavelet Energy Function Capability in Flood Risk Estimation in Compare with Traditional Methods

Document Type : Research Article

Authors

• S. Shahabi
• M.J. Khanjani

Shahahid Bahonar University of Kerman

https://doi.org/10.22067/jsw.v0i0.30326

Abstract

In this paper a method to perform Estimation of Flood Risk (EFR) is presented when the assumption of stationary is not important (or not valid). A wavelet transform model is developed to EFR. A full series is applied to EFR using energy function of wavelet. The data were decomposed into some details and an approximation through different wavelet functions and decomposition levels. The approximation series was employed to EFR. This was performed using daily maximum discharge data from of the Polroud River in the north of Iran. In this way, the data from 1956 to 2007 were evaluated by wavelet analysis. The study shows that wavelet full series model results (density function) are too small compared with the results of combined method and they are both lesser than traditional methods (AM and PD). In other hand, the results of energy function method are closed to the combined method when they are compared with the full series data results. These wavelet models were assessed with the AM and PD methods. The concrete result of this paper is that, the watershed hydrologic conditions and nature of the data are very important parameters to improve FFA and to select the best method of analysis.

Keywords

• Wavelet transform
• Polroud River
• Time Series

20.1001.1.20084757.1394.28.6.7.7

References

1- سازمان آب منطقه ای گیلان، دفتر مطالعات پایه. 1390. گزارش طرح تحقیقات کاربردی. تحلیل فراوانی سیلاب با استفاده از تبدیل موجک‌ها بر روی رودخانه پلرود رودسر.

2- شهابی س.، حسامی کرمانی م.ر.، حسین نژاد ع. و پنج علیزاده ب. 1391. تحلیل فراوانی سیلاب با استفاده از تبدیل موجک. فصلنامه ی علمی – پژوهشی مهندسی آبیاری و آب ایران 9: 88-77.

3- Adamowski J. A. 2008. Development of a Short-term River Flood Forecasting Method for snowmelt Driven Floods based on wavelet and cross-wavelet analysis. Journal of hydrology, 353: 247-266.

4- Adamowski J., and Chan, H. F. 2011. A Wavelet Neural Network Conjunction Model for Groundwater Model Forecasting. Journal of Hydrology, 407: 28-40.

5- BoggessA., and Narcowich F. J. 2009. A First Course inWavelets with Fourier Analysis. John Wiley, New Jersey.

6- Box G. E. P., and Cox, D. R. 1964. An Analysis of Transformations. Journal of the Royal Statistical Society, 127(B):211-252.

7- Chow V., Maidment D., andMays L. 2013. Applied hydrology. 2nded. McGraw-Hill Press,New York.

8- Googleearth (6.2.2.6613). 2012. (visited 23 October,2013).

9- HaddadK.,and Rahman A. 2012. Regional Flood Frequency Analysis in eastern Australia: Bayesian GLS Regression-Based Methods whithin Fixed Region and ROI Framework-Quantile Regression vs. Parameter Regression Technique. Journal of Hydrology. 430-431:142-161.

10- KarthikeyanL., andKumar D. N. 2013. Predictability of non-stationary time series using wavelet and EMD based ARMA models. Journal of Hydrology. 502:103-119.

11- LecrecM., and Ouarda T. B. M. J. 2007. Non-stationary Regional Flood Frequency Analysis at Ungaged Sites. Journal of Hydrology. 343: 254-265.

12- Ozger Mehmet., Mishra Ashok K., and Singhm Vijay P. 2010. Scaling Characteristics of Precipitation Data in Conjunction with Wavelet Analysis. Journal of Hydrology. 395: 279-288.

13- Pellegrini M., Sini F., and Taramasso A. C. 2012. Wavelet – Based Automated Localization and Classification of Peaks in Streamflow Data Series. Computers and Geosciences. 40: 200-204.

14- Rao A. R., and Hamed, K. H. 2000. Flood Frequency Analysis. Dehli.

15- Rossi A., Massei N., and Laignel B. 2011. A Synthesis of the Time-Scale Variability of Commonly Used Climate Indices Using Continues Wavelet Transform. Global and planetary change. 78: 1-13.

16- Sang Y. F. 2013. A review on the applications of wavelet transform in hydrology time series analysis. Atmospheric Research. 122: 8-15.

17- ShuC., and Ouarda T. M. B. J. 2008. Regional Flood frequency Analysis at Ungaged Sites Using the AdaptiveNeuro- Fuzzy Interface System. Journal of Hydrology. 349: 31-43.

18- Shu C., and Burn D. H. 2004. Homogeneous Pooling Group Delineation for Flood Frequency Analysis Using a fuzzy Expert System with Genetic Enhancement. Journal of Hydrology. 291: 132-149.

19- Srinivas V. V., Tripathi S., Rao A. R., and Govindaraju R. S. 2008. Regional Flood Frequency Analysis by Combining Self-Organizing Feature Map and Fuzzy Clustering. Journal of Hydrology. 348: 148-166.

20- Subramanya K. 2008. Engineering Hydrology. McGraw-Hill Press.

21- The Math Works. 2009. MATLAB, Version 7.8.0.347(R2009a).

22- Villarini G., Smith J. A., Serinaldi F., Jerad B., Bates P. D., and Krajewski W. F. 2009. Flood Frequency Analysis for nonstationary Annual Peak Records in an Urban Drainage Basin. Advances in water resources. 32: 1255-1266.