| تعداد نشریات | 282 |
| تعداد نشریات سازمان | 80 |
| تعداد شمارهها | 3,230 |
| تعداد مقالات | 35,784 |
| تعداد مشاهده مقاله | 54,010,216 |
| تعداد دریافت فایل اصل مقاله | 93,168,616 |
ارزیابی کارکرد مدل های برنامه ریزی ژنتیک و خودهمبسته میانگین متحرک در پیش بینی آبدهی روزانه در آبخیز امامه | ||
| پژوهش های آبخیزداری | ||
| مقاله 2، دوره 32، شماره 2 - شماره پیاپی 123، تیر 1398، صفحه 2-18 اصل مقاله (978.34 K) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmej.2018.123630.1157 | ||
| نویسندگان | ||
| محبوبه معتمدنیا* 1؛ کمال کریمی زارچی2؛ احمد نوحه گر3؛ مریم صابری اناری4؛ آرش ملکیان5 | ||
| 1دکترای علوم و مهندسی آبخیزداری | ||
| 2دانشجوی کارشناسی ارشد کنترل و مدیریت بیابان، دانشکده منابع طبیعی، دانشگاه یزد | ||
| 3استاد گروه آموزش،برنامه ریزی و مدیریت محیط زیست، دانشکده محیط زیست، دانشگاه تهران، کرج | ||
| 4مدرس دانشگاه فنی و حرفه ای، یزد | ||
| 5دانشیار گروه احیای مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران، کرج | ||
| چکیده | ||
| کمبودمنابعآبو توجه به توسعهی پایدار،تأمینآبرا برای همهی نیازهایموجودناممکنکردهاست. از آنجا که پیشبینیدقیقجریان رودهادر مدیریتمنابعآب اهمیتبسزایی دارد، آبدهی رود با کاربرد مدلهای برنامهریزی ژنتیک و خودهمبستهی میانگین متحرک در آبخیز امامه، استان تهران مدلسازی و پیشبینی شد. از دادههای درازمدت باران، دما، آبدهی، رطوبت نسبی و تبخیر استفاده شد. نتایج نشان داد که برنامهریزی ژنتیک خطای کمتری دارد و توانستهاست بهخوبی آبدهی مشاهدهیی را تخمین بزند. مدل 54 با ورودیهای دما، باران، و تأخیرهای باران تا دو روز، و رطوبتنسبی و تبخیر و تأخیر جریان تا دو روز،بهترین مدل با خطای 0/001، 0/031، و 0/009 در مرحلهی آموزش، و 0/001، 0/032، و 0/009 در مرحلهی آزمایشبود. علاوه بر این، خطای مدلهای خطی خودهمبستهی میانگین متحرک بسیار بیشتر است، و نهتنها در آبدهیهای بیشتر، بلکه در آبدهیهای کم همکارکرد مناسبی ندارد، و نتوانستهاست نتیجهی رضایتبخشی بهدست دهد. استفاده از مدل برنامهریزی ژنتیک بهدلیل دقت بسیار زیاد با عملگرهای اصلی و دادههای بهمعیارشده توصیه میشود. | ||
| کلیدواژهها | ||
| برنامهریزی ژنتیک؛ آبخیز امامه؛ مدل سازی جریان رود؛ مدل های خودهمبستهی میانگین متحرک | ||
| عنوان مقاله [English] | ||
| An Assessment the Performance of Genetic Programming and Auto Regresive Moving Average on the Daily Discharge Prediction (Case study: the Amameh Watershed) | ||
| نویسندگان [English] | ||
| Mahboobeh Motamednia1؛ Kamal Karimi Zarchi2؛ Aahmad Nohegar3؛ Maryam Saberi Anari4؛ Arash Malekian5 | ||
| 1Ph.D. of Watershed Management Science and Engineering | ||
| 2M.Sc., student of management and control of desert, natural resources and desert studies, The Head of the Department of Natural Resources, Bafgh, Yazd, Iran | ||
| 3Professor of Learning, Programming and Environment Management Department, Environment Faculty, Tehran University, Karaj | ||
| 4Instructor of Technical and Vocational University, Yazd, Iran | ||
| 5Associate Professor, Faculty of Natural Resources, University of Tehran, Karaj | ||
| چکیده [English] | ||
| Shortage of water resources and the growing concern about the sustainable development have made the water supply for all of the potential needs nearly impossible.As an accurate prediction of river discharge is very important in water resources management, the development of a model to predict discharge has been carried out using the genetic programming and auto regression moving average on the Amameh Watershed located in the Province of Tehran. The long-term rainfall, temperature, discharge, relative humidity, and evaporation data have been used. Satisfactorily, the results showed that genetic programming had a lower error and could estimate the observed discharge. Furthermore, the number 54 model with inputs of temperature, rain, the delay in rainfall of up to two days, relative humidity, evaporation, and the delay in discharge of up to two days were considered as the best fit model with the errors of 0.001, 0.031 and 0.009 in the training stage and 0.002 , 0.032, and 0.009 at the testing stage respectively. On the other hand, the linear auto regression moving average models showed a much higher error; they could neither predict the high discharge, nor low flow and have not been able to provide satisfactory results. Therefore, the application of a genetic programming model is recommended due toits high precision with the main operators and the standardized data. | ||
| کلیدواژهها [English] | ||
| Amameh Watershed, auto regressive moving average, genetic programming, river flow modeling | ||
| مراجع | ||
|
Ahmadi F, Radmanesh F, Mirabbasi Najaf Abadi R. 2015. Comparison between genetic programming and support vector machine methods for daily river flow forecasting (Case study: Barandoozchay River). Journal of Water and Soil. 28(6): 1162–1171. (In Persian). Aytek A, Asce M, Alp M.2008. An application of artificial intelligence for rainfall-runoff modeling. Hydrology and Earth System Sciences. 117(2): 145–155. Bashari M, Vafakhah M. 2011. Comparison of different time series analysis methods for forecasting monthly discharge in the Karkheh Watershed, Journal of Irrigation and Water Engineering. 1(2): 75–86. (In Persian). Box GE P, Cox DR. 1964. An analysis of transformations, Journal of the Royal Statistical Society. Series B. 26: 211–252. Chathield K. 2010. An Introduction to the analysis of time series, translated by Hossein Ali Nirmandan and Seyed Abolghasem Gavinia, Mashhad Ferdowsi University Press. 290 pp. (In Persian). Cramer S, Kampouridis M, Freitas A. 2018. Decomposition genetic programming: An extensive evaluation on rainfall prediction in the context of weather derivatives, Applied Soft Computing Journal. 70: 208–224. Danandeh Mehr A, Kahya E, Olyaie E. 2013. Streamflow prediction using linear genetic programming in comparisonwith a neuro-wavelet technique, Journal of Hydrology. 505: 240–249. Danandeh Mehr A, Kahya E, Yerdelen C. 2014. Linear geneticprogrammingapplicationforsuccessive-station monthlystreamflowprediction, Journal of Computers and Geosciences.70(2014):63–72. Darbandi S. Dinpajouh Y. Zeinali S. 2014. Efficiency study of the system dynamics model to simulate the rainfall–runoff (Case Study: Lighvan Watershed), Journal of Water and Soil, 28(1):127-138 (In Persian). Dorado J, Rabunal JR, Pazos A, Rivero D, Santos A, Puertas J. 2003. Prediction and modeling of therainfall-runoff transformation of a typical urban basin using ANN and GP, Appl. Artificial Intelligent. 17: 329–343 pp. Farboudfam N. Ghorbani M.A. Alami M.T. 2009. River flow prediction using Genetic programming (Case Study: Lighvan River Watershed), Journal of Soil and Water Science. 19(1): 107–122. (In Persian). Ghorbani MA, Khatibi R, Aytek A, Makarynskyy O, Shiri J. 2010. Sea water level forecasting using genetic programming and artificial neural networks. Computers and Geoscience. 36(5): 620–627. Golnarkar S, Poureza-Bilondi M, Khashei A, Amirabadizadeh M. 2017. Assessment of basin hydrological components by modified conceptual continuous rainfall-ran off SCS-CN, 2017. Journal of Water and Soil Conservation. 24(1): 1–23. (In Persian). Guven A. 2009. Linear genetic programming for time-series modeling of daily flow rate. Journal of Earth System Science, 118(2):157–173. Hosseini SM, Mahjouri N. 2016. Integrating support vector regression and a geomorphologic artificial neural network for daily rainfall-runoff modeling, Applied Soft Computing. 38: 329–345. Huo Z, Feng S, Kang S, Huang G, Wang F, Guo P. 2012. Integrated neural networks for Monthly River flow estimation in arid inland basin of Northwest China, Journal of Hydrology. 420–421: 159–170. Jayawardena AW, Muttil N, Fernando TMKG. 2005. Rainfall-runoff modelling using geneticprogramming, International Congress on Modelling and Simulation Society ofAustralia and New Zealand December 2005, New Zealand. 1841–1847 pp. Karamouz M, Araghinejad Sh. 2005. Advanced Hydrology, Amir Kabir press. 480pp. (In Persian). Khu ST, Liong SY, Babovic V, Madsen H, Muttil N. 2001. Genetic programming and its application in real- time runoff forming. Journal of American Water Resource Associate. 37(2): 439–451. Mahdavi M. 2002.Applied hydrology, Tehran University Press. 364 p. (In Persian). Masoodi A, Parsamehr P, Salmasi F, Pureskandar S. 2012. Regression analysis, genetic programming and ANN to predict discharge coefficient of compound broad crested weir, Journal of Water and Soil. 26(4): 933–942. (In Persian). Massoudian SA. 2003. Investigating the geographical distribution of rainfall in Iran, using periodic factor analysis, Journal of Geography and Development. 1: 79–88. (In Persian). Mendez MC, Wenceslao G, Manuel PF, Manuel JLP, Roman L. 2004. Modelling of the monthly and daily behaviour of the runoff of the Xallas river using Box-Jenkins and neural networks methods, Journal of Hydrology. 296: 38–58. Moatamednia MA, Nohegar A, Malekian Saberi M, Karimi Zrchi K. 2017. Runoff prediction using intelligent models, Ecohydrology. 4(4): 968–955. (In Persian). Rahimikhoob A, Mahmoodi A. 2012. Estimating actual evapotranspiration in a catchment using artificial neural networks with minimum climatic data (Case study: Emame Representative Catchment, Iran-Water Resources Research. 7 (4): 51–61. (In Persian). Salajegheh A, Fathabadi A, Mahdavi M. 2009. Investigation on the efficiency of neuro-fuzzy method and statistical models in simulation of rainfall-runoff process, Journal of Range and Watershed Management, 1(62): 65–97. Sarangi A, Bhattacharya AK. 2005. Comparison of artificial neural network and regression models for sediment loss prediction from Banha watershed in India, Agricultural water management. 28(4): 373–385. Sharifi AR, Dinpashoh Y, Fakheri-Fard A, Moghaddamnia AR. 2013. Optimal combination of variables for runoff simulation in the Amameh Wtershed using gamma test, Water and Soil Science. 23(4): 59–72. (In Persian). Solaimani K. 2009. Rainfall-runoff prediction based on artificial neural network (A Case Study: Jarahi Watershed), American-Eurasian Journal of Agriculture and Environment. Science. 5 (6): 856–865. Soltani A, Ghorbani MA, Fakherifard A, Darbandi S, Farsadizadeh D. 2010. Genetic programming and its application in modeling the rainfall-runoff process, Journal of Soil and Water. 1(4): 61–71. (In Persian). Tarazkar MH, Sedghamiz A. 2008. Monthly discharge forecasting for Karkheh River by using time series and artificial Intelligent traits, Pajouhesh and Sazandegi. 80: 51–58. (In Persian). Valipour M, Banihabib ME, Behbahani SMR. 2013. Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez Dam Reservoir, Physical Geography Research. 476: 433–441. Varoonchotikul P. 2003. Flood forecasting using artificial neural networks, vrije universiteit unesco-ihe Institute for Water Education. 116 pp. Wang W, Men Ch, Lu W. 2009. Online prediction model based on support vector machine, Neuro computing. 71: 550–558. Wu CL, Chau KW, Li YS. 2009. Methods to improve neural network performance in daily flows prediction, Journal of Hydrology. 72 (1–4): 80–93. Yosefi M, Talebi A, Poorshareiati R. 2014. Application of artificial intelligence in water and soil science, Yazd University Press. 534 p. (In Persian). Zahiri AR, Dehghani AA, Hezarjeribi A. 2012. Determination of stage discharge curve for laboratory and river compound channels applying genetic algorithm. Journal of Water and Soil Conservation. 19(2):179–192. (In Persian). Zahiri A, Azamathulla MD. 2014. Comparison between linear genetic programming and M5 tree models to predict flow discharge in compound channels. Neural Computing and Application. 4:413–420. | ||
|
آمار تعداد مشاهده مقاله: 850 تعداد دریافت فایل اصل مقاله: 940 |
||