Volume 8, Issue 16 (2-2018)
J Watershed Manage Res 2018, 8(16): 53-64 |
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(2018). Investigation the Ability of Artificial Neural Network in Simulation of Rainfall-Runoff Process under the Climate Change Conditions (Case Study: Pashakola Babol Dam Basin). J Watershed Manage Res. 8(16), 53-64. doi:10.29252/jwmr.8.16.53
URL: http://jwmr.sanru.ac.ir/article-1-903-en.html
URL: http://jwmr.sanru.ac.ir/article-1-903-en.html
Abstract: (4596 Views)
River flow forecasting plays an important role in planning, management and operation of water resources. To achieve this goal and according to the phenomenon of global warming, it is necessary to simulate the daily time series of rainfall and runoff for future periods. Therefore, it is important to survey the detection of climate change event and its impact on precipitation and runoff in the basin. In the first step of this research, using Mann-Kendall trend statistical test, climate change event in the Pashakola Babol basin in Mazandaran province is confirmed. The results of the survey on 36 years daily mean temperature data, reflect the increasing trend in average temperature in the basin. In the second step, the LARS-WG model under general circulation models HadCM3 and A2 scenario is used to generate the daily rainfall time series in the future period. In order to rainfall data, minimum temperature, maximum temperature and sunshine hours is entered in the model for 12-year base period (2004-2015) and Then daily rainfall time series in the basin is predicted for the next 10-year period (2016-2025). In the third step, the artificial neural network model is used to simulate the process of rainfall - runoff in the climate change condition and to generate the daily runoff time series in the future period. Finally, in order to enhance the capability of the artificial neural network model in predicting the daily runoff, besides the predicted daily precipitation data, the rainfall and runoff data one day before as effective factors on the current day runoff is also entered in the model and using rainfall and runoff neurons. Correlation coefficient was obtained equal to 0.8. This correlation coefficient is significant at 1% and show the ability of model to simulate rainfall–runoff process.
References
1. Ahmadi, F., Y. Dinpazhouh, A. Fakherifard, K. Khalili and C. Darbandi. 2015. Comparsion of nonlinear time series models and genetic programming in daily river flow forecasting (Case study: Barandoezchay river in Orumieh). Journal of Soil and Water Conservation, 22 (1): 151-169 (In Persian).
2. Camici, S., L. Brocca, F. Melone and T. Moramarco. 2014. Impact of climate change on flood frequency using different climate models and downscaling approaches. Journal of Hydrologic Engineering, ASCE, 19 (8): 1-15. [DOI:10.1061/(ASCE)HE.1943-5584.0000959]
3. Dayhoff, J.E. 1990. Neural network principles. Prentice-Hall International, U.S.A, 197 pp.
4. Fiseha, B.M., A.M. Melesse, E. Romano, E. Volpi and A. Fiori. 2012. Statistical downscaling of precipitation and temperature for the Upper Tiber Basin in Central Italy. International Journal of Water Sciences, 1 (3): 1-10. [DOI:10.5772/52890]
5. Ghafari, Gh.A. and M. Vafakhah. 2013. Simulation of rainfall-runoff process using artificial neural network and adaptive neuro-fuzzy interface system(case study: Hajighoshan watershed). Journal of Watershed Management Research, 4 (8): 120-136 (In Persian).
6. Hagh Talab, N., M. Goudarzi, M. Habibi Nokhandan, A.R. Yavari and H.R. Jafari. 2013. Climate modeling of Tehran and Mazandaran provinces using climate model LARS-WG and to compare changes in northern and southern fronts of Central Alborz. Environmental Sciences, 1(15): 1-13 (In Persian).
7. Hasanpour Kashani, M., M.A. Ghorbani, Y. Dinpazhouh and S. Shahmorad. 2015. Rainfall-Runoff simulation in the Navrood river basin using truncated volterra model and artificial neural networks. Journal of Watershed Management Research, 6 (12): 1-10 (In Persian).
8. Hejarizadeh, Z., S.M. Hoseyni and A.R. Karbalaee Doree. 2015. Simulation of climate variables in Semnan province by scenarios from general circulation models (HadCM3). Journal of Geography of Environmental Hazards, 4 (15): 1-24 (In Persian).
9. Hosseini, S.H., M.A. Ghorbani and A.R. Massah Bavani. 2015. Raifall-Runoff modelling under the climate change condition in order to project future streamflows of Sufichay Watershed. Journal of Watershed Management Research, 6 (11): 1-14 (In Persian).
10. Huang, S., F. Hattermann, V. Krysanova and A. Bronstert. 2013. Projections of climate change impacts on river flood conditions in Germany by combining three different RCMs with a regional eco- hydrological model. Climate Change, 116 (3-4): 631-663. [DOI:10.1007/s10584-012-0586-2]
11. Jahangeer, A.R., M. Raeini - Sarjaz and M.Z. Ahmad. 2009. Comparison of artificial neural networks (ANN) simulation of rainfall-runoff process with HEC-HMS model in Kardeh watershed. Journal of Soil and Water (Agricultural Science and Technology), 22 (2): 72-84 (In Persian).
12. Karamooz, M. and Sh. Araghinezhad. 2005. Advanced hydrology. Amirkabir University Press, Tehran, 464 pp (In Persian).
13. Khazaee, M. and M.R. Mirzaee. 2013. Compare performance of monthly discharge prediction Using artificial neural network and time series methods. Soil Conservation and Watershed Management Research Institute, 5 (2): 74-84 (In Persian).
14. Liu, L., Z. Liu, X. Ren, T. Fischer and Y. Xu. 2011. Hydrological impacts of climate change in the Yellow River Basin for the 21 st century using hydrological model and statistical downscaling model. Quaternry International, 244 (2): 211-220. [DOI:10.1016/j.quaint.2010.12.001]
15. Nazari, M., H. Ghasemieh, S.J. Sadatinejad and A. Vali. 2015. An investigation of artificial neural network performance in modeling rainfall-runoff and its comparison with multivariate regression method: a case study of Babolroud river. International Bulletin of Water Resources & Development, 3 (3): 119-133 (In Persian).
16. Omidvar, K. and M. Azhdarpoor. 2013. Comparision artificial neural network and model HEC-HMS in the estimation of rainfall - runoff in the Basin of Heart grand river. Journal of Geographical Research, 27 (4): 139-160 (In Persian).
17. Rasco, P., L. Szeidl and M.A. Semenov. 1991. A serial approach to local stochastic models. Journal of Ecological Modeling, 57: 27-41. [DOI:10.1016/0304-3800(91)90053-4]
18. Razzaghian, H., K. Shahedi and M. Habibnejad-roshan. 2016. Evaluation of the climate change effect on Babol-rood watershed runoff using IHACRES model. Journal of Irrigation & Water Engineering, 7 (26): 159-172 (In Persian).
19. Sanikhani, H., M.R. Gohardoust and M. Sadeghi. 2016. Assessment of climate change effect on the runoff in Gharechay watershed in Markazi province. Journal of Watershed Management Research, 7 (13): 12-22 (In Persian). [DOI:10.18869/acadpub.jwmr.7.13.22]
20. Sazabshargh Consulting Engineers. 2010. Updating the atlas of water resources (Mazandaran and Gwilan east rivers basin), 1: 262 pp (In Persian).
21. Semenov, M.A. and E.M. Barrow. 2002. LARS-WG: A stochastic weather generator for use in climate impact studies. User Manual, 27 pp.
22. Sheidaeian, M. 2013. Assessment of climate change effect on the rice net irrigation requirement (Case Study: Tajan Plain). Master Thesis of Irrigation and Drainage Engineering, Sari Agricultural Science and Natural Resources University, Sari, Iran, 226 pp (In Persian).
23. Zahmatkesh, Z., M. Karamouz, E. Goharian and S. Burian. 2015. Analysis of the effects of climate change on urban storm water runoff using statistically downscaled precipitation data and a change factor approach. Journal of Hydrologic Engineering, ASCE, 20 (7): 1-11. [DOI:10.1061/(ASCE)HE.1943-5584.0001064]
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