پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,532 |
| تعداد مقالات | 70,504 |
| تعداد مشاهده مقاله | 124,121,591 |
| تعداد دریافت فایل اصل مقاله | 97,228,748 |
کاربرد مدلهای فصلی سری زمانی در پیشبینی جریان ماهانه ورودی به مخزن سدهای یامچی و سبلان در حوضه آبخیز قره‌سو، اردبیل | ||
| تحقیقات آب و خاک ایران | ||
| دوره 51، شماره 10، دی 1399، صفحه 2651-2663 اصل مقاله (1.4 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2020.305309.668658 | ||
| نویسندگان | ||
| امین کانونی* 1؛ سهیلا اورجی2 | ||
| 1گروه مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران | ||
| 2گروه مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی | ||
| چکیده | ||
| پیشبینی حجم آب ذخیره شده در سدهای مخزنی در دورههای آتی، نقش مهمی در برنامهریزی و مدیریت بهرهبرداری بهینه از سامانههای منابع آب دارد. در این مطالعه، از روش تحلیل سریهای زمانی برای پیشبینی جریان ماهانه ورودی به سدهای مخزنی یامچی و سبلان در استان اردبیل استفاده شد. دادههای دبی جریان ماهانه اندازهگیری شده در ایستگاههای هیدرومتری واقع در محل ورود آب به سد، طی سالهای 94-1373 به مدت 21 سال تهیه و برای ساخت و آزمون مدل مناسب، به کار برده شد. پس از ایستا نمودن سری دادهها، با توجه به نمودارهای خودهمبسته (ACF) و خودهمبسته جزئی (PACF)، ساختارهای مدل فصلی تشخیص داده شدند و پس از مقایسه آنها با توجه به معیارهای آکائیکه (AIC)، آکائیکه اصلاح شده (AICC) و اطلاعات بیزی (BIC)، مدل مناسب برای هر یک از ایستگاههای هیدرومتری انتخاب شد. با برازش مدل به دادههای مشاهداتی، پارامترهای هر مدل تعیین و کفایت مدلهای منتخب نیز با آزمونهای تشخیصی بررسی گردید. نتایج نشان داد مدل ARIMA(1,0,0)(0,1,1)12 و ARIMA(1,1,1)(0,1,1)12 بهترتیب برای دادههای دبی ماهانه ایستگاه یامچی و اربابکندی، دارای کمترین مقدار شاخصهای آماری ریشه میانگین مربعات خطا (RMSE) و میانگین قدرمطلق خطا (MAE) بوده و دارای بیشترین ضریب تعیین است. مقدار این شاخصها در مدل مربوط به ایستگاه هیدرومتری یامچی بهترتیب برابر 04/1، 606/0 و 63/0 و برای ایستگاه هیدرومتری اربابکندی به ترتیب برابر 35/1، 8/0 و 74/0 بهدست آمد. لذا مدلهای منتخب، جریان ماهانه ورودی به مخزن سدهای یامچی و سبلان را با دقت خوبی پیشبینی میکند. همچنین مقایسه نتایج پیشبینی شده با دادههای مشاهداتی نشان داد در پیشبینی مقادیر حد بالای دبی، مدلهای منتخب از دقت بالایی برخوردار نیستند. | ||
| کلیدواژه‌ها | ||
| بهرهبرداری مخزن؛ پیشبینی؛ جریان ورودی ماهانه؛ سریهای زمانی؛ مدل فصلی | ||
| عنوان مقاله [English] | ||
| Application of Seasonal Time Series Models for Prediction of Monthly Inflow to Yamchi and Sabalan Reservoirs in Qarasu Catchment, Ardabil | ||
| نویسندگان [English] | ||
| Amin Kanooni1؛ Soheila Urji2 | ||
| 1Water Engineering Department, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili | ||
| 2Department of Water Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili | ||
| چکیده [English] | ||
| Predicting volume of water stored in reservoirs in the future periods plays an important role in planning and managing the optimal use of water resources systems. In this study, time series analysis method was used to predict the monthly inflow to Yamchi and Sabalan reservoirs in Ardabil province. The monthly flow data measured at hydrometric stations, located at the dam's entrance for 21 years (1994 to 2015) were used to build and test an appropriate model. The structures of the seasonal models were identified according to the auto-correlation charts (ACF) and partial auto-correlation (PACF), and then the appropriate model for each hydrometric station was selected based on the Akaike Information Criterion (AIC), Akaike Information Criterion Correction (AICC) and Bayesian Information Criterion (BIC). By fiting the model to the observational data, the parameters of each model were determined and the adequacy of the selected models was also examined by diagnostic tests. The results showed that ARIMA (1,0,0)(0,1,1)12 and ARIMA (1,1,1)(0,1,1)12 models, respectively for the monthly flow data of Yamchi and Arbabkandi stations have the lowest root mean square error (RMSE) and mean absolute error (MAE) and the highest determination coefficient. The values of these indicators in the model related to Yamchi hydrometric station were 1.04, 0.606 and 0.63, respectively, and for Arbabkandi hydrometric station were 1.35, 0.8 and 0.74, respectively. Therefore, the selected models accurately predict the monthly inflows to Yamchi and Sabalan reservoirs. Comparing the predicted results with the observational data showed that the selected models are not very accurate in predicting high discharge values. | ||
| کلیدواژه‌ها [English] | ||
| Forecasting, Monthly Inflow, Reservoir operation, Seasonal model, time series | ||
| مراجع | ||
|
Abbasi, M., Araghinejad, S. and Ebrahimi, K. (2019). Evaluation of Moving Average Pre-processing Approach to Improve the Efficiency of Support Vector Regression Model for Inflow Prediction. Iranian Journal of Soil and Water Research, 50(1), 247-258. (In Farsi). Abudu, S., Cui, CL., King, JP. and Abudukadeer, K. (2010). Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River, China. Water Science and Engineering, 3(3), 269-281. Adnan, R. M., Yuan, X., Kisi, O. and Yuan, Y. (2017). Streamflow forecasting of Astore River with Seasonal Autoregressive Integrated Moving Average model. European Scientific Journal, 13(12), 145-156. Ahmadi, F., Dinpajoh, Y., Fakheri Fard, A. and Khalili, K. (2014). Comparing linear and nonlinear time series models in river flow forecasting (case study: Baranduz-chai river). Journal of Irrigation Science and Engineering, 37(1), 93-105. (In Farsi). Ayare, B.L. and Dhekale, B.S. (2015). Multiplicative seasonal ARIMA modeling of monthly stream flow of Choriti river. International Journal of Agricultural Engineering, 8(1), 97-102. Bashari, M. and vatankhah, M. (2011). Comparison of Different Time Series Analysis Methods for Forecasting Monthly Discharge in Karkheh Watershed. Irrigation and Water Engineering, 1(2), 75-86. (In Farsi). Box, G.E.P. and Jenkins, G.M. (1970). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day. Box, G.E.P. and Jenkins, G.M. (1976). Time series analysis, forecasting and control. (revised ed.). San Francisco: Holden-Day. Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (2008). Time series analysis: forecasting and control. (4th ed.). New Jersey: Wiley and Sons. Brewer, M., Butler, A. and Cooksley, S.L. (2016). The relative performance of AIC, AICC and BIC in the presence of unobserved heterogeneity. Methods in Ecology and Evolution, 7, 679-692. Dastorani, M., Mirzavand, M., Dastorani, M.T. and Sadatinejad, S.J. (2016). Comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition. Nat Hazards, 81, 1811–1827. Elganiny, M.A. and Eldwer, A.E. (2016). Comparison of Stochastic Models in Forecasting Monthly Streamflow in Rivers: A Case Study of River Nile and its Tributaries. Journal of Water Resource and Protection, 8, 143-153. Fernandez, C., Vega, J.A., Fonturbel, T. and Jimenez, E. (2009). Streamflow drought time series forecasting: a case study in a small watershed in North West Spain. Stochastic Environmental Research and Risk Assessment, 23, 1063–1070. Gharde, K.D., Kothari, M. and Mahale, M. (2016). Developed Seasonal ARIMA Model to Forecast Streamflow for Savitri Basin in Konkan Region of Maharshtra on Daily Basis. Journal of Indian Society Coastal Agriculture Research, 34(1), 110-119. Han, P., Wang, P., Tian, M., Zhang, Sh., Liu, J. and Zhu, D. (2012). Application of the ARIMA Models in Drought Forecasting Using the Standardized Precipitation Index. In 6th International Conference on Computer and Computing Technologies in Agriculture, 19-21 Oct., Zhangjiajie, China. Hu, C.H., Wu, Z.N., Wang, J.J. and Liu, L. (2011). Application of the Support Vector Machine on precipitation-runoff modelling in Fenhe River. International Symposium on Water Resource and Environmental Protection (ISWREP), IEEE, 2, 1099-1103. Khazaei, M. and Mirzaei, M. (2013). Comparison prediction performance of monthly discharge using ANN and time series. Watershed Engineering and Management, 5(2), 74-84. (In Farsi). Mirzapour, H. and Tahmasebipour, N. (2018). Predicting the monthly discharge of KAKAREZA River using time-series models ARIMA seasonal. Wetland Ecobiology, 9 (4), 75-86. (In Farsi). Mirzavand, M. and Ghazavi, R. (2015). A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods. Water Resources Management, 29(4), 1315-1328. Modarres, R. and Eslamian, S.S. (2006). Streamflow time series modeling of Zayandehrud river. Iranian Journal of Science & Technology, Transaction B, Engineering, 30(B4), 567-570. Moeeini, H., Bonakdari, H. and Ebtehaji, I. (2017). Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach. Journal of Earth System Science, 126(18), 1-13. Moeeni, H., Bonakdari, H. and Abdolahi, S. (2016). Performance evaluation of some statistical and soft computing models to predict river flow. Iran Water Resources Research, 12(3), 201-206. (In Farsi). Mojiri, H. and Halabian, A. (2019). Prediction of the Surface Runoff in Semirom Mehregerd Watershed Using ARIMA Model. Journal of watershed Management Science, 13(46), 74-81. (In Farsi). Nakhaee, M. and Mirarabi, A. (2010). Flood Forecasting in Sombar River by Time series Analysis using Box-Jenkins Model. Journal of Engineering Geology, 4(1), 901-915. (In Farsi). Salas, J. D. and Smith, R. A. (1981). Physical basis of stochastic models of annual flows. Water Resources Research, 17(2), 428-430. Salas, J.D., Delleur, J.W., Yevjevich, V. and Lane, W.L. (1980). Applied Modeling of Hydrologic Time Series. (1st ed.). Water Resources Publication, Colorado: Littleton. Shathir, A.K. and Mohammed Saleh, L.A. (2016). Best ARIMA models for forecasting inflow of HIT station. Basrah Journal for Engineering Sciences, 16(1), 62-71. Singh, M., Singh, R. and Shinde, V. (2011). Application of Software Packages for Monthly Stream Flow Forecasting of Kangsabati River in India. International Journal of Computer Applications, 20(3), 7-14. Tadesse, K.B. and Dinka, M.O. (2017). Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa. Journal of Water and Land Development, 35, 229-236. Thomas, H. A. and Fiering, M. B. (1962). Mathematical synthesis of streamflow sequences for the analysis of river basins by simulation. In: Design of water resources systems, Cambridge: Harvard University Press. Vahdat, S.F., Sarraf, A., Shamsnia, A. and Shahidi, N. (2011). Prediction of monthly mean Inflow to Dez Dam reservoir using time series models (Box-Jenkins). 2011 International Conference on Environment and Industrial Innovation, 4-5 Jun., Asia-Pacific Chemical, Biological & Environmental Engineering Society, Kuala Lumpur. pp. 162–166. Valipour, M. (2015). Long‐term runoff study using SARIMA and ARIMA models in the United States. Meteorological Applications, 22(3), 592-598. Wagena, M.B., Georing, D., Collick, A.S., Bock, E., Fuka, D.R., Buda, A. and Easton, Z.M. (2020). Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models. Environmental Modelling & Software, 126:104669. Wang, H.R., Wang, C., Lin, X. and Kang, J. (2014). An improved ARIMA model for precipitation simulations. Nonlinear Processes Geophysics, 21, 1159–1168. Wang, J., Du, Y.H. and Zhang X.T. (2008). Theory and application with seasonal time series. (1th ed.). Nankai: Nankai University Press. Wang, J., Hu, J., Ma K. and Zhang, Y. (2015). A self-adaptive hybrid approach for wind speed forecasting. Renewable Energy, 78, 374-85. Wang, Sh., Feng, J. and Liu, G. (2013). Application of seasonal time series model in the precipitation forecast. Mathematical and computer modeling, 58, 677-683. Wang, W. 2006. Stochasticity, Nonlinearity and Forecasting of Streamflow Processes. Amsterdam: IOS. Wong, H.W., Zhang R. and Xia, J. (2007). Non-parametric time series models for hydrological forecasting. Journal of Hydrology, 332, 337-347. Yeh, H. and Hsu, H. (2019). Stochastic Model for Drought Forecasting in the Southern Taiwan Basin. Water, 11, 1-15. | ||
|
آمار تعداد مشاهده مقاله: 424 تعداد دریافت فایل اصل مقاله: 352 |
||