| تعداد نشریات | 284 |
| تعداد نشریات سازمان | 82 |
| تعداد شماره‌ها | 2,784 |
| تعداد مقالات | 31,679 |
| تعداد مشاهده مقاله | 40,110,093 |
| تعداد دریافت فایل اصل مقاله | 17,604,341 |
روندیابی هیدروگراف سیل با استفاده از مدل عددی Mike11 و ماشین بردار پشتیبان (مطالعۀ موردی: رودخانۀ سیمره) | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| مقاله 6، دوره 21، شماره 78، اردیبهشت 1399، صفحه 79-98 اصل مقاله (1.49 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/idser.2019.125250.1377 | ||
| نویسندگان | ||
| سعیده ملک زاده1؛ علی آرمان* 2؛ آرش آذری2 | ||
| 1کارشناس ارشد گروه مهندسی آب، دانشکده علوم و مهندسی کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه رازی، کرمانشاه، ایران | ||
| 2استادیار دانشکده علوم و مهندسی کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه رازی، کرمانشاه، ایران | ||
| چکیده | ||
| روندیابی سیل در مقاطع مختلف رودخانه و کاربرد آن در طراحی سیستم های هشدار سیل و همچنین پیش بینی حجم سیلاب در مقاطع پایین دست رودخانه بااهمیت است. هدف از این تحقیق، بررسی و مقایسۀ مدل های MIKE11 و ماشین بردار پشتیبان (SVM) در روندیابی سیل در مقاطع پایین دست رودخانه است. در مطالعۀ حاضر ابتدا بر اساس اطلاعات فیزیکی و هیدرولیکی مسیر به روندیابی هیدرولیکی هیدروگراف سیل بین دو ایستگاه هیدرومتری هلیلان و تنگ سازبن در بازهای به طول 67 کیلومتر از رودخانه سیمره واقع در استان ایلام پرداخته شد. برای این منظور، در این محدوده 365 مقطع برای روندیابی در مدل Mike11 در نظر گرفته شد. پس از آن از مدل SVM برای پیش بینی هیدروگراف سیل در خروجی بازه بر اساس اطلاعات هیدروگراف سیل ورودی به بازه استفاده شد. این دو مدل هریک با یک هیدروگراف سیل واسنجی و با دو هیدروگراف سیل صحت سنجی شدند. هیدروگراف سیل در بالادست با دورۀ بازگشت های مختلف به این دو مدل داده شد و مدل سیل را در مقاطع خروجی بازه با دورۀ بازگشت های مختلف پیش بینی کرد. مقایسۀ نتایج به‌دست آمده از دو مدل نشان داد که به طور کلی دقت روش Mike11 نسبت به روش ماشین بردار پشتیبان بیشتر است. اگر معیار فقط پیش‌بینی دبی اوج در محدودۀ دبی های تاریخی باشد، مدل SVM تا حدودی نتیجه بهتری نسبت به مدل Mike11 دارد. اما برای پیش بینی وقایع حدی با دورۀ برگشت های بیشتر از ده سال، دقت لازم را ندارد. با در نظر گرفتن شاخص‌های آماری RMSE و NRMSE و NASH که برای ارزیابی نتایج، به جای استفاده از دبی اوج، از تمام دبی‌های هیدروگراف استفاده می‌کند، مدل Mike11 نتایج قابل قبول‌تری نسبت به مدل SVM به دست میدهد. در پیش بینی هیدروگراف وقایع حدی با دوره برگشت های مختلف، مدل Mike11 عملکرد بهتری نسبت به مدل SVM دارد. | ||
| کلیدواژه‌ها | ||
| رودخانه‌های سیلابی؛ روش‌های عددی؛ روندیابی سیلاب؛ مقاطع رودخانه‌ای؛ هوش مصنوعی | ||
| عنوان مقاله [English] | ||
| Flood Hydrograph Routing using Mike11 Numerical Model and Artificial Intelligence System (Case Study: Seymareh River) | ||
| نویسندگان [English] | ||
| Saeedeh Malekzadeh1؛ Ali Arman2؛ Arash Azari2 | ||
| 1Water Engineering branch, Campus of Agriculture and Natural Resources, Razi University | ||
| 2Compus oF Agriculture and Natural Resources, Razi University,Kermanshah | ||
| چکیده [English] | ||
| Introduction Using hydrological information and physical and hydraulic characteristics of the river route for flood routing in different cross sections of the river, impact of forecasting on the occurrence and flow peak flow and successful implementation of flood alarm systems and forecasting flood volume downstream of the river. Given the importance of predicting flood hydrographs, especially in floodplain rivers, and the lack of sufficient information and statistics at some river basins, this may result in hydraulic methods in such rivers. This study aimed to compare the results of flood routing using the numerical model MIKE11 and the support vector machine method. This comparison was based on three parts: peak discharge, flood volume and baseline hydrograph time. Methodology In this study, based on physical and hydraulic information of the route, hydraulic routing of flood hydrographs between two hydrometric stations of Holeilan and Sazebon was conducted in a distance of 67 km from Semareh River in Ilam province, Iran. For this purpose, 365 cross-sections with an approximate distance of 200 m were considered for flow routing. The geometrical data of these sections and their physical characteristics in terms of their distance (in kilometers) were entered in the MIKE11 upstream interval. River bed gradiation curves were used to estimate the roughness coefficient in each section. These coefficients were optimized during model calibration and validation. In addition to the main river, discharge included two major sub-branches located along the river. The flow rate of these branches was defined as sub-flow of internal boundaries in the model. For upstream boundary conditions, hourly hydrograph data of Holeilan station and for downstream boundary conditions, flood hydrograph data and discharge values of rating-curve station at the last river crossing were entered. The SVM model was then used to predict the flood hydrograph at the output of the interval based on the input flood hydrograph data. Based on the delay time of flood passage between Holeilan and Sazebon stations, flood hydrograph data at Holeilan station located upstream of 0, 2, 4, 6, 8, 10, 12 and 14 hours delays as input layers and hydrograph discharge values at the location of the Sazebon station was considered as the output layer and the model was calibrated and validated using recorded hydrographs. Finally, the results of these two models were compared. Flood hydrographs were also upstream with different return periods and flood hydrographs were predicted at interval outflows with different return periods. Results and Discussion Based on the results of both calibration and validation, it was found that the support vector machine method predicted the peak discharge relatively better than that of the MIKE11 method, but with considering statistical indices, such as RMSE which calculated all hydrograph discharges, the results of the flood hydrograph prediction in MIKE11 model had relative advantages over the support vector machine model. This model also simulated the volume of floods more accurate. However, in order to compare the behavioral pattern of predicted flood hydrographs with different return periods, especially floods with the peak discharge more than the peak discharge hydrographs used in the calibration and validation phase, flood hydrograph values with period various returns as inputs to the study area were entered into each model at Holeilan station and flood hydrographs were predicted at the output of the interval and at the corresponding station location with the corresponding return period. Because the classification of data in the training phase is adjusted to discharges less than 25 years, the SVM model was able to predict only hydrographs with 2, 5 and 10 year return periods. But after the calibration and validation, the Mike11 model was able to predict the hydrograph well with the return periods of 2, 5, 10, 25, 50 and 100 years. Conclusions The results showed that the SVM model was somewhat better than the Mike11 model if the only criterion was to predict peak flow within the range of historical discharges. But this model was not accurate for predicting partial events with a return period of more than ten years. Given the statistical indices of RMSE, NRMSE, and NASH, which use all hydrograph discharges to evaluate results rather than using peak discharge, the Mike11 model provided better results than the SVM model. The Mike11 model performed better than the SVM model in predicting partial event hydrographs with different return periods. Acknowledgment The authors of the article extend their thanks reviewers for reviewing and improving the structure of the article, and also to the National Surveying Agency for providing the maps of the Seymareh River. | ||
| کلیدواژه‌ها [English] | ||
| Artificial Intelligence, Flood Rivers, Flood Routing, Numerical Methods, River Cross Sections | ||
| مراجع | ||
|
Adamowski, J., & Prasher, Sh. O. (2012). Comparison of machine learning methods for run off forecasting in mountainous watersheds with limited data. Journal of Water and Land Developmen , 17(VII-XII): p. 89-97
Ahmadi, F., Radmanesh, F., & Mirabbasi N. 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): p. 1162-1171. (in Persian)
Asghari, J., & Rostami, R. (2017). Monthly forecast of the Simine Rood river using a Support Vector Machine. 4th National Conference on Applied Research in Civil Engineering, Architecture and Urban Management. Jan. 26-27, Tehran, Iran. (in Persian)
Dastoorani, M. T. (2007). Investigating the application of computational intelligence models in simulation and prediction during flood flows. Journal of Science and Technology of Agriculture and Natural Resources, 11(40): p. 27-36.(in Persian)
Dawson, C. W., Abrahart, R. J., Shamseldin, A. Y., & Wilby, R. L. (2006). Flood estimation at ungauged sites using artificial neural networks. Journal of Hydrology, 319, p. 391-409.
Delphi, M. (2012). Application of characteristics method for flood routing (Case study: Karun river). Journal of Geology and Mining Research, 4(1): p. 8-12.
Derakhshan-Alamdarloo, P., Molazadeh, S. A., Azhdari, M. M., & Azizian, A. (2016). Comparison of the results of MIKE11 and HEC-RAS models in non-steady flow simulation (case study: Gareh Aghaj river). 15th National Hydraulic Conference of Iran, Dec. 14. Qazvin, Iran. (in Persian)
Hoseinazdeh, M., Emamgholizadeh, S., Ganji, Z., & Samadi, H. (2015). Comparison of flood routing by MIKE11 and HEC-RAS models in Bazoft river. 15th National Hydraulic Conference of Iran, Dec. 14. Qazvin, Iran. (in Persian)
Isazadeh, M., Ahmadzadeh, H., Ghorbani, M.A. (2015). Assessment of kernel functions performance in river flow estimation using support vector machine. Journal of Water and Soil Conservation, 23(3): p. 69-89. (in Persian)
Kakaei-Lafadani, E., Moghaddam-Nia., A., Ahmadi, A., Jajarmizadeh, M., & Ghafari, M. (2013). Stream flow simulation using SVM, ANFIS and NAM models (A case study). Journal of Applied Science, 2(4): p. 86-93.
Kamel, A. H. (2008). Application of a hydrodynamic Mike11 model for the Euphrates river in Iraq. Journal of Slovak of Civil Engineering. 2, p. 1-7.
Karahan, H., Iplikci, S., Yaser, M., & Gurarslan, G. (2014). River flow estimation from upstream flow records using Support Vector Machines. Journal of Applied Mathematics, Article
Kisi, O., & Cimen, M . (2011). A wavelet-support vector machine conjunction model for monthly streamflow forecasting. Journal of Hydrology, 399(1): p. 132-140.
Rahimi-Shooshtari, M., & Mahmoodian-Shooshtari, M. (2011). Investigation of flood routing using Mike 11 software (case study: Ahwaz-Farsiat river Karun river). 6th National Congress on Civil Engineering. Apr. 26. Semnan, Iran. (in Persian)
Rouhi, M., Heidari, M., Ebrahim Sani., M., & Abedini, A. A. (2015). Evaluation of computer models MIKE11 and HEC-RAS in numerical simulation of flood flow. Water Engineering Conference & Exhibition. Oct. 17. Tehran, Iran. (in Persian)
Timbadiya, P. V., Patel, P. L., & Porey, P. D. (2014). One-dimensional hydrodynamic modeling of flooding and stage hydrographs in the lower Tapi River in India. Current Science, 106(5): p. 708-716.
Zhang, X., Srinivasan, R., & Van Liew, M. (2009). Approximating SWAT model using artificial neural network and support vector machine. Journal of American Water Resources Association, 45(2): p. 460-474. | ||
|
آمار تعداد مشاهده مقاله: 569 تعداد دریافت فایل اصل مقاله: 365 |
||