برآورد ضریب آبدهی سرریزهای کلید پیانویی انحنادار با استفاده از ترکیب رگرسیون بردار پشتیبان و الگوریتم های ملخ و کرم شب تاب

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه مهندسی عمران- مهندسی آب و سازه های هیدرولیکی، دانشکده مهندسی عمران، دانشگاه سمنان

2 کارشناسی ارشد مهندسی آب و سازه‌های هیدرولیکی، دانشکده مهندسی عمران، دانشگاه سمنان

چکیده

سرریزهای کلیدپیانویی نوع جدیدی از سرریزها هستند که در جهت افزایش ظرفیت تخلیه سدها و کانال­ها طراحی می­شوند. در صورتی­که کلیدهای تشکیل­دهنده این مدل سرریز بر روی کمانی از یک دایره قرار بگیرند، آن را سرریز کلیدپیانویی انحنادار می­نامند. در این پژوهش عملکرد سه مدل هوشمند رگرسیون بردار پشتیبان (SVR)، رگرسیون بردار پشتیبان- کرم شب­تاب (SVR-FA) و رگرسیون بردار پشتیبان- ملخ (SVR-GOA) برای پیش­بینی میزان آبدهی سرریزهای کلیدپیانویی انحنادار مورد ارزیابی قرار گرفته­ است. ضریب تعیین (R2)، میانگین مربعات خطا (MAE) ، جذر میانگین مربعات خطا (RMSE) و شاخص پراکندگی (SI) چهار شاخص آماری می­باشند که برای تعیین دقت مدل­های  هوشمند به کار گرفته شده ­است. نتیجه این معیارهای ارزیابی در دوره آزمون نشان می­دهد که مدل SVR-GOA با مقادیر 99275/0، 01202/0، 00026/0 و 00046/0 نسبت به مدل SVR-FA با مقادیر 95666/0، 03844/0، 00200/0 و 00342/0 و SVR با مقادیر 94249/0، 04013/0، 06027/0 و 00410/0 به ترتیب برای شاخص­های R2،MAE ،RMSE  و SI از دقت بیشتری در پیش­بینی آبدهی سرریز کلیدپیانویی انحنادار برخوردار است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Estimating Discharge Coefficient of Curved Piano Key Overflows Using Combination of Support Vector Regression and Grasshopper and Firefly Algorithms

نویسندگان [English]

  • hojat karami 1
  • Alireza Rezaei Ahvanooei 2
1 Assistant Professor, Department of Civil Engineering, Semnan University, Semnan, Iran
2 MSc, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University
چکیده [English]

Piano-Key weirs are a new type of overflow that are designed to increase the drainage capacity of dams and canals. If the keys forming this overflow model are placed on an arc of a circle, it is called curved piano-key weir. In this research, the performance of three models of Intelligent Support Vector Regression (SVR), Support Vector Regression- Firefly (SVR-FA) and Support Vector Regression- Grasshopper (SVR-GOA) to predict curved piano-key weir flow rate were evaluated. Determination Coefficient (R2), Mean Squared Error (MAE), Root Mean Squared Error (RMSE), and Scattering Index (SI) are four statistical indicators that are used to determine the accuracy of intelligent models. The result of these evaluation criteria during the test period is that the SVR-GOA model with values ​​of 0.99275, 0.01220, 0.00026 and 0.00046 compared to the SVR-FA model with values ​​of  0.95666, 0.03844, 0.00200 and 0.00342 and SVR with values ​​of 0.94249, 0.04013, 0.06027 and 0.00410 for R2, MAE, RMSE and SI indicators, are more accurate in predicting curved piano-key weir flow rate

کلیدواژه‌ها [English]

  • Support vector regression
  • Grasshopper Algorithm
  • Firefly Algorithm
  • Curved Piano-Key Weir
  • Discharge coefficient
روشنگر، ک.، ماجدی اصل، م.، اعلمی، م و شیری، ج. ارزیابی تاثیر تغییرات زاویه سیکل قوسی بر ضریب دبی سرریزهای کنگرهای قوسی و کلید پیانویی قوسی. تحقیقات آب و خاک ایران. 49. 2: 341-351.
صفرزاده، ا. و نوروزی، ب. 1393. هیدرودینامیک سه­ بعدی سرریزهای کلیدپیانویی انحنادار در پلان. مجله هیدرولیک. 9 .3 : 61- 79.
Anderson, R.M and Tullis, B. 2011. Influence of Piano Key weir geometry on discharge. Proc. Intl. Conf. Labyrinth and Piano Key Weirs, Liège B, 75-80. CRC Press, Boca Raton FL.
Anderson, R.M., and Tullis, B. 2012. Comparison of Piano Key and rectangular Labyrinth weir hydraulics. J. Hydr. Engin. 138.4:358-361.
Azimi, H., Bonakdari, H and Ebtehaj, I. 2017. A highly efficient gene expression programming model for predicting the discharge coefficient in a side weir along a trapezoidal canal. Irrigation and drainage. 66.4:655-666.
Aljarah, I., Ala’M, A. Z., Faris, H., Hassonah, M. A., Mirjalili, S and Saadeh, H. 2018. Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cognitive Computation, 10.3: 478-495.
Goel, A. 2013. Modeling aeration of sharp crested weirs by using support vector machines. World Academy of Science, Engineering and Technology. 7.12: 2620-2625.
Heidari, A. A., Faris, H., Aljarah, I and Mirjalili, S. 2019. An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Computing, 23.17: 7941-7958.
Juma, I. A., Hussein, H and AL-Sarraj, M. 2014. Analysis of hydraulic characteristics for hollow semi-circular weirs using artificial neural networks. Flow Measurement and Instrumentation. 38: 49–53.
Kabiri-Samani, A and Javaheri, A. 2012. “Discharge coefficient for free and submerged flow over piano key weirs”. Journal of Hydraulic Research. 50.1: 114-120.
Karami, H., Karimi, S., Rahmanimanesh, M and Farzin, S. 2017. Predicting discharge coefficient of triangular labyrinth weir using support vector regression, support vector regression-firefly, response surface methodology and principal component analysis. Flow Measurement and Instrumentation, 55: 75-81.
Khoshbin, F., Bonakdari, H., Ashraf Talesh, S.H., Ebtehaj, I., Zaji, A.H and Azimi, H. 2016. Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs. Engineering Optimization. 48.6 :933-948.
Laugier, F. 2007. Design and construction of the first Piano Key Weir (PKW) spillway at the Goulours dam. Hydropower & Dams. 14.5: 94-101.
Laugier, F., Lochu, A., Gille, C., Leite Ribeiro, M and Boillat, J.L. 2009. Design and construction of a labyrinth PKW spillway at St-Marc Dam. J. Hydropower Dams. 15.5: 100-107.
Leite Ribeiro, M., Bieri, M., Boillat, J.L., Schleiss,A.J., Singhal, G and Sharma, N. 2012. Discharge capacity of Piano Key Weirs. J.Hydraulic Eng. 138: 199-203.
Leite Ribeiro, M., Pfister, M., Boillat, J.L., Schleiss, A.J and Laugier, F. 2012. Piano key weirs as efficient spillway structure. Proc. 24nd ICOLD congress on Large Dams, Kyoto, Japan, Q.94, R.13.
Leite Ribeiro, M., Pfister, M., Schleiss, A.J and Boillat, J.L. 2012. Hydraulic design of A-type Piano Key weirs. J. Hydr. Res. 50.4: 400-408.
Mehr, A. D., Nourani, V., Khosrowshahi, V.K and Ghorbani, M.A. 2019. A hybrid support vector regression–firefly model for monthly rainfall forecasting. International Journal of Environmental Science and Technology. 16.1: 335-346.
Olyaie, E., Heydari, M and Banejad, H. 2018b. Estimating Discharge Coefficient of PK-Weir Under Subcritical Conditions Based on High-Accuracy Machine Learning Approach.Iran J Sci Technol Trans Civ Eng. 1-13.
Olyaie, E., Heydari, M., Banejad, H and Chau, K.W. 2018a. A laboratory investigation on the potential of computational intelligence approaches to estimate the discharge coefficient of piano key weir. Journal of Rehabilitation in Civil Engineering. 6: 1-20.
Pai, P.F., Hong, W.C. 2005. Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electric Power Systems Research. 74.3: 417-425.
Parsaie, A and Haghiabi, A.H. 2014. Assessment of some famous empirical equation and artificial intelligent model (MLP, ANFIS) to predicting the side weir discharge coefficient. Applied Research in Water and Wastewater. 2: 75-79.
Roushangar, K., Alami, M.T., Shiri, J and Asl, M.M. 2018. Determining discharge coefficient of labyrinth and arced labyrinth weirs using support vector machine. Hydrology Research. 49.3: 924-938.
Saremi, S., Mirjalili, S and Lewis, A. 2017. Grasshopper optimisation algorithm: theory and application. Adv Eng Softw. 105: 30–47                                                                                                                                                               
Schleis, A. 2011. Labyrinth and piano key weirs-PKW. CRC Press, Leiden. 17-24.
Vapnik, V.N. 1995.The Nature of Statistical Learning Theory. Springer, New York.
Yang, X.S. 2009. Firefly Algorithms for Multimodal Optimization. Stochastic Algorithms: Foundations and Applications 5792. 169-178.
Zhang, X., Wang, J. and Zhang, K. 2017. Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm. Electric Power Systems Research. 146: 270-285.
Zhao, S and Wang, L. 2010. Support vector regression based on particle swarm optimization for rainfall forecasting. In Computational Science and Optimization (CSO), Third International Joint Conference. 28.2: 484-487.
Zounemat-Kermani, M and Mahdavi-Meymand, A. 2019. Hybrid meta-heuristics artificial intelligence models in simulating discharge passing the piano key weirs. Journal of Hydrology. 569: 12-21.