ارزیابی روش‌های مختلف وزن‌دهی برای پیش‌بینی عمق آبشستگی پایین‌دست سازه‌های تثبیت بستر

نوع مقاله : مقاله کامل (پژوهشی)

نویسندگان

1 گروه آب، دانشکده کشاورزی دانشگاه گنبد کاووس

2 گروه آب، دانشکده کشاورزی، دانشگاه گنبد کاووس

چکیده

تخمین عمق آبشستگی سازه از اهمیت زیادی برخوردار است. محققین بر مبنای کارهای آزمایشگاهی و صحرایی روابط تجربی متعددی ارائه داده‌اند ولی تاکنون رابطه‌ای که در شرایط مختلف نتایج قابل قبولی داشته باشد شناخته نشده است. در این پژوهش دقت روابط مختلف تجربی (منفرد) در دو مرحله‌ی قبل و بعد از اصلاح اریبی مورد ارزیابی قرار گرفتند. نتایج نشان می‌دهد قبل و بعد از اصلاح اریبی به‌ترتیب روابط منفرد مؤسسه‌ی ملی و ماسون با میانگین مربعات خطای 87/0 و 23/0 متر بیشترین دقت را دارند. ترکیب روابط منفرد با مدل‌های ترکیبی نشان می‌دهد از بین روش‌های مستقیم در مرحله‌ی قبل و بعد از اصلاح اریبی به‌ترتیب روش GRA و EWA با میانگین مربعات خطای 25/0 و 23/0 متر بیشترین دقت را داشته است. خطای روش‌های ترکیبی غیر مستقیم AICA و BICA در مرحله‌ی قبل و بعد از اصلاح اریبی مشابه بهترین رابطه‌ی منفرد است و نتوانسته‌اند نتایج روابط منفرد را بهبود بخشند. نتایج روش موضعی (KNN) و روش هوش مصنوعی (LS-SVM) قبل و بعد از اصلاح اریبی برابر بوده و عمق آبشستگی را با دقت بیشتری نسبت به روابط منفرد برآورد کرده‌اند. مقایسه‌ی روش‌های مختلف ترکیبی در مرحله‌ی قبل و بعد از اصلاح اریبی نشان داد که کم‌ترین خطا با میانگین مربعات خطای 18/0 و 19/0 متر به‌ترتیب مربوط به LS-SVM و KNN می‌باشد. در این پژوهش مشخص شد که ترکیب روابط منفرد حداکثر عمق آبشستگی با استفاده از روش‌های مختلف ترکیبی می‌تواند دقت پیش‌بینی را بهبود بخشد.

کلیدواژه‌ها


Andrawis, R.R., Atiya, A.F. and El-Shishiny, H. (2011). Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition. International Journal of Forecasting, 27(3), 672-688.
Arsenault, R. and Brissette, F. (2016). Multi-model averaging for continuous streamflow prediction in ungauged basins. Hydrological Sciences Journal, 61(13), 2443-2454.
Arsenault, R., Gatien, Ph., Renaud, B., Brissette, F. and Martel, J.L. (2015). A comparative analysis of 9 multi-model averaging approaches in hydrological continuous streamflow simulation. Journal of Hydrology, 529, 754–767.
Ayoubi, S., Taghizadeh, R., Namazi, Z., Zolfaghari, A. and Roustaee Sadrabadi, F. (2016). The Comparison of k-NN and ANN for Digital Mapping of Salinity in Chahafzal Ardekan. Journal of Water and Soil Science (Journal of Science and Technology of Agriculture and Natural Resources), 20(76), 59-71. (in Persian)
Bates J.M. and Granger C.W.J. (1969). The combination of forecasts. Journal of the Operational Research Society, 20(4), 451-468.
Bormann N.E. and Julien, P.Y. (1991). Scour downstream of grade-control structures. Journal of Hydraulic Engineering, 117(5), 579-94.
Burnham, K. P. and Anderson, D. R. (2002). Model selection and multi model inference: a practical information-theoretic approach, Second ed. Springer-Verlag, New-York, United-States, 487p.
Chou, J.S. and Pham, A.D. (2013). Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. Construction and Building Materials, 49, 554-563.
D’Agostino, V. and Ferro, V. (2004). Scour on alluvial bed downstream of grade control structures. Journal of Hydraulics Engineering, 130(1), 24-37.
De Menezes, L. M., Bunn, D. W. and Taylor, J. W. (2000). Review of guidelines for the use of combined forecasts. European Journal of Operational Research, 120(1), 190-204.
Dehghani, A.A. and Abdi, Dehkordi, M. (2014). Application of genetic algorithms in the optimization of empirical relations to estimate of geometrical characteristics of the scour hole downstream of grade control structures. Modares Civil Engineering Journal, 14(2), 165-211. (In Persian)
Diks, C.G.H. and Vrugt, J.A. (2010). Comparison of point forecast accuracy of model averaging methods in hydrologic applications. Stochastic Environmental Research and Risk Assessment, 24(6), 809-820.
Dobarco, M.R., Arrouays D., Lagacherie P., Ciampalini, R. and Saby N. P. (2017). Prediction of topsoil texture for Region Centre (France) applying model ensemble methods. Geoderma, 298, 67-77.
Draper, D. (1995). Assessment and propagation of model uncertainty. Journal of the Royal Statistical Society. Series B (Methodological), 45-97.
Granger, C.W. and Ramanathan, R. (1984). Improved methods of combining forecasts. Journal of Forecasting, 3(2), 197-204.
Guven, A. and Gunal, M. (2008). Prediction of scour downstream of grade-control structures using neural networks. Journal of Hydraulic Engineering, 134(11), 1656-1660.
Hansen, B.E. (2008). Least-squares forecast averaging. Journal of Econometrics, 146(2), 342-350.
INCYTH LHA. Instituto Nacional de Ciencia y Técnicas Hidricas Laboratorio de Hidraulica. (1981). Estudio sobre modelo del aliviadero de la presa de piedra. Informe Final. DOH-044-03-82. Ezeiza. Argentina.
Jalali, V. R. and Homaee, M. (2011). Introducing a Nonparametric Model Using k-Nearest Neighbor Technique for Predicting Soil Bulk Density. JWSS-Isfahan University of Technology, 15(56), 181-191. (In Persian)
Jovanović, R. Ž., Sretenović, A.A. and Živković B. D. (2015). Ensemble of various neural networks for prediction of heating energy consumption. Energy and Buildings, 94, 189-199.
Kiran, N.R. and Ravi, V. (2008). Software reliability prediction by soft computing techniques. Journal of Systems and Software, 81(4), 576-583.
Machado, L.I. (1980). Formulas para calcular o limite da erosao em leitos rochosos ou granulares. XIII Seminario Nacional de Grandes Barragems.Rio de Janeiro.Brazil. Apr. 35-52.
Malone, B.P., Minasny, B., Odgers, N.P. and McBratney, A.B. (2014). Using model averaging to combine soil property rasters from legacy soil maps and from point data. Geoderma, 232, 34-44.
Martins, R.B. F. (1975). Scouring of rocky river beds by free jet spillways. International Water Power and Dams Construction, 27(4), 152-153.
Mason, P.J. and Arumugam, K. (1985). Free jet scour below dams and flip buckets. Journal of Hydraulic Engineering, 111(2), 220-235.
Mehraein, M. and Meraji, S.H. (2017). Application of PSO Algorithm in estimating the formation of scour holes caused by 2D wall jets. Modares Civil Engineering Journal, 17(4), 229-239. (In Persian)
Moazami, S., Noori, R., Salimian, M., Momeni, M.R. and Vesali Naseh, M. R. (2017). Evaluation of Support Vector Machine Performance for Carbon Monoxide Prediction. Modares Civil Engineering Journal, 17(3), 195-202. (In Persian)
Mossa, M. (1998). Experimental study on the scour downstream of grade-control structures. Proc., 26th Convegno di Idraulica e Costruzioni Idrauliche, 581-94.
Newbold, P. and Granger, C.W.J. (1974). Experience with forecasting univariate time series and the combination of forecasts (with discussion). Journal of the Royal Statistical Society Series A (General), 137(2), 131-149.
Nourani, V., Elkiran, G., Abdullahi, J. and Tahsin, A. (2019). Multi-region modeling of daily global solar radiation with artificial intelligence ensemble. Natural Resources Research, 28(4), 1217-1238.
Padarian, J., Minasny, B., McBratney, A.B. and Dalgliesh, N. (2014). Predicting and mapping the soil available water capacity of Australian wheatbelt. Geoderma Regional, 2, 110-118.
Panahi, S., Farsadizadeh, D., Hosseinzadeh Dalir, A., Salmasi, F. and Nazemi, A.H. (2014). Investigating the effect of cup angle on scour dimensions in the downstream of submerged jar. Iran Water Research, 7(13), 185-195. (In Persian)
Pourreza Bilondi, M., Khashei-Siuki, A. and Sadeghi, Tabas S. (2015). Daily rainfall-runoff modeling with Least Square Support Vector Machine (LS-SVM). Journal of Water and Soil Conservation, 21(6), 293-304. (In Persian)
Saifi, A. (2011). Development of an expert system for estimating daily reference evapotranspiration using a backup vector machine (SVM) and comparing its results with ANFIS, ANN and experimental methods. Master's thesis, Faculty of Agriculture, Tarbiat Modares University. 147 p. (In Persian)
Seifert, D., Sonnenborg, T.O., Refsgaard, J.C., Højberg, A. L. and Troldborg, L. (2012). Assessment of hydrological model predictive ability given multiple conceptual geological models. Water Resources Research, 48(6), 1-16.
Shafai Bajestan, M. (2011). Theoretical and practical principles of hydraulic transmission of sediment. First edition, Shahid Chamran University Press, Ahvaz. (In Persian)
Sharma, A. and O'Neill, R. (2002). A nonparametric approach for representing interannual dependence in monthly streamflow sequences. Water resources research, 38(7), 51-60.
Shu, C. and Burn, D. H. (2004). Artificial neural network ensembles and their application in pooled flood frequency analysis. Water Resources Research, 40(9), 1-10.
Siwek, K., Osowski, S. and Szupiluk, R. (2009). Ensemble neural network approach for accurate load forecasting in a power system. International Journal of Applied Mathematics and Computer Science, 19(2), 303-315.
Spiliotis, E., Petropoulos, F. and Assimakopoulos, V. (2019). Improving the forecasting performance of temporal hierarchies. Plos One, 14(10), 1-21.
Suykens, J.A. and Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9(3), 293-300.
Tahmasebi Biragani, Y., Yazdandoost, F. and Ghalkhani, H. (2016). Flood Forecasting Using Artificial Neural Networks: An Application of Multi-Model Data Fusion technique. Journal of Hydraulic Structures, 2(2), 62-73.
Vapnik, V. (1998). Statistical learning theory. Wiley, New York. 768P.
Yakowitz, S. (1993). Nearest neighbor regression estimation for null-recurrent Markov time series. Stochastic Processes and their Applications, 48(2), 311-318.
Zhang, Z., Han, H., Cui, X. and Fan, Y. (2020). Novel application of multi-model ensemble learning for fault diagnosis in refrigeration systems. Applied Thermal Engineering, 164, 1-11.
Zhou, Z.H., Wu, J, and Tang, W. (2002). Ensembling neural networks: many could be better than all. Artificial intelligence, 137(1-2), 239-263.
Zolfaghari, A.A., Taghizadeh-Mehrjardi, R., Moshki, A.R., Malone, B.P., Weldeyohannes, A.O., Sarmadian, F. and Yazdani, M.R. (2016). Using the nonparametric k-nearest neighbor approach for predicting cation exchange capacity. Geoderma, 265, 111-119.