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Comparison of the performance of stochastic models in the generation of synthetic monthly flows data: A case study on Marun river | ||
| Journal of Applied Research in Water and Wastewater | ||
| مقاله 6، دوره 6، شماره 2، مهر 2019، صفحه 117-125 اصل مقاله (1.23 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22126/arww.2019.1405 | ||
| نویسندگان | ||
| Mostafa Bayesteh؛ Arash Azari* | ||
| Department of Water Engineering, Faculty of Science and Agricultural Engineering, Razi University, Kermanshah, Iran. | ||
| چکیده | ||
| One of the most important issues in planning and managing water resources is the accurate estimation of monthly input discharge of the reservoirs in the future years, which is always associated with uncertainty. To cover these uncertainties, synthetic stream flow data generation models have been used by various researchers to generate stochastic time series. The computational basis of different stochastic models for generating monthly data has been different and this can have a significant effect on their performance. Therefore, selection of the best model of stochastic data generation for accurate planning and management of a water resource system is one of the major concerns of water resources specialists. In this research, the performance of parametric models of synthetic stream flow generation including Thomas-Fiering, Fragment and ARMA (1,1) and ARMA (1,2) combined with Valencia-Schaake and Mejia and Rousselle models were compared and evaluated. For this purpose, 30 years data of monthly discharge of Marun river in Khuzestan province were used and 900 synthetic monthly flow time series were generated using each of the models mentioned above. Based on the obtained results, the ARMA (1,2) model combined with the Valencia-Schaake model was recognized as the best model, considering the very desired performance in preserving the statistical parameters of historical data and generating maximum and minimum discharges related to wet and dry periods in different probabilities. This model can be used with greater confidence to analyze river systems and reservoirs, manage drought and apply water rationing rules in future drought conditions. | ||
| کلیدواژهها | ||
| Synthetic data generation؛ Parametric methods؛ Thomas-Fiering؛ Monthly flow Marun river | ||
| مراجع | ||
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Adeli A., Fathi Moghadam M., Musavi Jahromi H., Using stochastic models for Synthetic time series generation and predicting the input of Talogh dam reservoir, International Journal of Analytical Research on Water Resources and Development 5 (2014) 1-13. Aksoy H., and Dahamsheh A., Markov chain-incorporated and synthetic data-supported conditional Synthetic neural network models for forecasting monthly precipitation in arid regions, Journal of Hydrology 562 (2018) 758-779. Castellarin A., Galeati G., Brandimarte L., Montanari A., Brath A., Regional flow-duration curves: Reliability for ungauged basins, Advances in Water Resources 27 (2004) 953-965. Dashora L., Singal S.K., Srivastav D.K., Software application for data driven prediction models for intermittent streamflow for Narmada River Basin, International Journal of Computer Applications 113 (2015) 9-17. Güldal V., and Tongal, H., Comparison of recurrent neural network, adaptive Neuro-Fuzzy inference system and stochastic models in Egirdir lake level forecasting, Water Resources Management 24 (2010) 105–128. Harms A.A., and Campbell T.H., An extension to the Thomas‐Fiering Model for the sequential generation of stream flow, Water Resources Research 3 (1967) 653-661. Hazen A., Storage to be provided in impounding reservoirs for municipal water supply, Transactions of the American Society of Civil Engineers 77 (1914) 1539-1669. Karamooz W., and Araghinejad S., Advanced hydrology, 1st ed., Amir Kabir University of Technology Publication, Tehran, (2006) 264-267. Lin G.F., and Lee F.C., An aggregation-disaggregation approach for hydrologic time series modeling, Journal of Hydrology 138 (1992) 543-557. Lohani A.K., Kumar R., Singh R.D., Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques, Journal of Hydrology 442 (2012) 23-35. Maheepala S., and Perera B.J.C., Monthly hydrologic data generation by disaggregation, Journal of Hydrology 178 (2012) 277-291. Mejia J.M., and Rousselle J., Disaggregation models in hydrology revisited, Water Resources Research 12 (1976) 185–186. Moeini H., Bonakdari H., Abdollahi S., Performance evaluation of some statistical models and soft computing in river flow forecasting, Iran Water Resources Research 3 (2017) 201-206. Montaseri M., and Adeloye A.J., Critical period of reservoir systems for planning purpose, Journal of Hydrology 224 (1999) 115- 136. Montaseri M., and Heydari J., Comparison of performance of stochastic models in generation of monthly flow and rainfall data, Iran Water Resources Research 3 (2016) 69-84. Porter J.W., and Pink B.J., A method of synthetic fragments for disaggregation in stochastic data generation, Proceedings in Hydrology and Water Resources Symposium, Australia (1991) 187-191. Prairie J., Nowak K., Rajagopalan B., Lall U., Fulp T., A stochastic nonparametric approach for streamflow generation combining observational and paleo reconstructed data, Water Resources Research 44 (2008) 1-11. Salas J.D., Analysis and modeling of hydrologic time series, In Handbook of Hydrology, Edited by Maidment, McGrow – Hill book CO., New York, (1993). Savic D., Burn D.H., Zrinji Z., A comparison of stream flow generation models for reservoir capacity-yield analysis, Water Resources Bulletin 25 (1989) 977-983. Turcotte D.L., Fractals and chaos in geology and geophysics, Cambridge university press, New York, (1997) 403. | ||
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