Use of analytical data and intelligent models in runoff precipitation simulation (Case study: Bazoft basin)

Document Type : Research Paper

Authors

1 Graduated with a PhD, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

2 Professor, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

3 Professor, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

4 Assistance Professor, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

Abstract

Today, the use of intelligent models in simulating runoff has been widely used in water resources management. In this study, in order to predict the daily flow time series of the Morghak hydrometric station in Karun basin, an intelligent model of artificial neural network combined with wavelet analysis has been used. For this purpose, the ERA-INTRIM observational and analytical precipitation time series for 16 years (1378-1382) was decomposed by wavelet transform into frequency subsets, then each subset separately as input data to the artificial neural network model was introduced. The results showed that the analytical data have a high ability to simulate runoff precipitation models and can be a good alternative to observation data of rainfall stations. Also, according to the results of the wavelet transform technique, it can be effective in improving the performance of the simple ANN model for the Bazoft basin by 38% on a daily scale and 72% on a monthly scale.

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Main Subjects

References

 

  • Almasi, P. Soltani, S. Goodarzi, M. Modarres, R., 2017. Assessment the Impacts of Climate Change on Surface Runoff in Bazoft Watershed. JWSS.; 20 (78) pp:39-52. DOI: 18869/acadpub.jstnar.20.78.39.

 

  • ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, 2000. Artificial neural networks in hydrology. II: Hydrologic applications. Journal of Hydrologic Engineering, 5(2), pp.124-137.

 

  • Azizian, A., Ramezani, H., 2019. Assessing the Accuracy of European Center for Medium Range Weather Forecasts (ECMWF) Reanalysis Datasets for Estimation of Daily and Monthly Precipitation. Iranian Journal of Soil and Water Research 50 (4), 777-791. DOI: 22059/ijswr.2018.261613.667962.

 

  • Bahroloum, R., Ramezani , H., Azizian, A. and Ababaei, B., 2020.Use of Gridded Weather Datasets in Simulation of Wheat Yield and Water Requirement (Case Study: Iran’s Qazvin Plain). Iranian journal of Ecohydrology 7 (3), 691-706. DOI: 22059/IJE.2020.303567.1339.

 

  • Belo‐Pereira, M., Dutra, E. and Viterbo, P., 2011. Evaluation of global precipitation data sets over the Iberian Peninsula. Journal of Geophysical Research: Atmospheres, 116(D20). DOI: 1029/2010JD015481.

 

  • Bengtsson, L. and Shukla, J., 1988. Integration of space and in situ observations to study global climate change. Bulletin of the American Meteorological Society, 69(10), pp.1130-1143. DOI: 1175/1520-0477(1988)069<1130:IOSAIS>2.0.CO;2.

 

  • Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, D.P. and Bechtold, P., 2011. The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the royal meteorological society, 137(656), pp.553-597. DOI: 1002/qj.828.

 

  • De Leeuw, J., Methven, J. and Blackburn, M., 2015. Evaluation of ERA‐Interim reanalysis precipitation products using England and Wales observations. Quarterly Journal of the Royal Meteorological Society, 141(688), pp.798-806. DOI: 1002/qj.2395.

 

  • Ma, L., Zhang, T., Frauenfeld, O.W., Ye, B., Yang, D. and Qin, D., 2009. Evaluation of precipitation from the ERA‐40, NCEP‐1, and NCEP‐2 Reanalyses and CMAP‐1, CMAP‐2, and GPCP‐2 with ground‐based measurements in China. Journal of Geophysical Research: Atmospheres, 114(D9). DOI: 1029/2008JD011178.

 

  • Nourani, V., Alami, M.T. and Aminfar, M.H., 2009. A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Engineering Applications of Artificial Intelligence, 22(3), pp.466-472. DOI: 1016/j.engappai.2008.09.003.

 

  • Nourani, V., Kisi, Ö. and Komasi, M., 2011. Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology, 402(1-2), pp.41-59. DOI: 1016/j.jhydrol.2011.03.002.

 

  • Nourani V., and Komasi M. 2013. A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff process. Journal of Hydrology, 490, pp. 41–55. DOI: 1016/j.jhydrol.2013.03.024.

 

  • Peña-Arancibia, J.L., van Dijk, A.I., Renzullo, L.J. and Mulligan, M., 2013. Evaluation of precipitation estimation accuracy in reanalyses, satellite products, and an ensemble method for regions in Australia and South and East Asia. Journal of Hydrometeorology, 14(4), pp.1323-1333. DOI: 1175/JHM-D-12-0132.1.

 

  • Pourhaghi, A., Solgi, A., Radmanesh, F., Shehni darabi, M. (2018). 'Hybrid Usage of The Wavelet ransform and Intelligent to Simulation River Flow (Case Study: KaKa Reza and Sarab seyed Ali rivers)', Irrigation and Water Engineering, 8(4), pp. 1-17. (in Persian).

 

  • Raziei, T., Bordi, I. and Pereira, L.S., 2011. An application of GPCC and NCEP/NCAR datasets for drought variability analysis in Iran. Water resources management, 25(4), pp.1075-1086.

 

 

  • Raziei, T. and Sotoudeh, F., 2017. Investigation of the accuracy of the European Center for Medium Range Weather Forecast (ECMWF) in forecasting observed precipitation in different climates of Iran. Journal of the earth and space physics, 43(1), pp.133-147. (in Persian).

 

  • Rhodes, R.I., Shaffrey, L.C. and Gray, S.L., 2015. Can reanalyses represent extreme precipitation over England and Wales?. Quarterly Journal of the Royal Meteorological Society, 141(689), pp.1114-1120. DOI: 1002/qj.2418.

 

  • Rubel, F. and Rudolf, B., 2001. Global daily precipitation estimates proved over the European Alps. Meteorologische Zeitschrift, 10(5), pp.407-418.

 

  • Schiemann, R., Lüthi, D., Vidale, P.L. and Schär, C., 2008. The precipitation climate of Central Asia—intercomparison of observational and numerical data sources in a remote semiarid region. International Journal of Climatology: A Journal of the Royal Meteorological Society, 28(3), pp.295-314. DOI: 10.1002/joc.1532

 

  • Sharghi E, Nourani V, Najafi H, Molajou A, 2018. Emotional ANN (EANN) and Wavelet-ANN (WANN) approaches for markovian and seasonal based modeling of rainfall-runoff process. Water Resources Management 32(10):3441-3456

 

  • Trenberth, E. and Olson, G., 1988. Evaluation of NMC global analyses: 1979-1987.

 

  • Wang W., and Ding S. 2003. Wavelet network model and its application to the predication of hydrology. Nat. Sci, 1(1):67–71.

 

  • Wang, A. and Zeng, X., 2012. Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau. Journal of geophysical research: atmospheres, 117(D5) DOI: 1029/2011JD016553.
Irrigation Sciences and Engineering
Volume 47, Issue 2
September 2024
Pages 17-30

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History

  • Receive Date: 05 January 2021
  • Revise Date: 11 February 2021
  • Accept Date: 14 February 2021
  • Publish Date: 22 August 2024

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