بررسی عمکلرد مدل هیبریدی ماشین بردار پشتیبان-الگوریتم گیاهان مصنوعی در تخمین جریان روزانه رودخانه ها(مطالعه موردی:حوضه دز)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری سازه‌های آبی/دانشکده کشاورزی، دانشگاه لرستان.

2 دانشیار /گروه مهندسی آب، دانشکده کشاورزی، دانشگاه لرستان.

3 استادیار/ گروه مهندسی آب، دانشکده کشاورزی، دانشگاه لرستان.

چکیده

برآورد دقیق جریان رودخانه‌های حوضه های آبریز نقش مهمی در مدیریت منابع آب‌ به‌ویژه تصمیمات صحیح در مواقع سیلاب و خشکسالی‌ دارد. در سالهای اخیر جهت برآورد جریان رودخانه‌ها روش های متنوعی در هیدرولوژی معرفی‌شده که مدل‌های هیبریدی هوش مصنوعی از مهم‌ترین آن‌ها است. در این پژوهش یک روش پیشنهادی هیبریدی تحت عنوان ماشین بردار پشتیبان- الگوریتم گیاهان مصنوعی مورد بررسی قرار داده و نتایج آن با مدل ماشین بردار پشتیبان-موجک مقایسه گردید. به منظور برآورد دبی رودخانه های حوضه آبریز دز، از آمار آبدهی روزانه ایستگاههای هیدرومتری واقع در بالادست سد طی دوره آماری(1397-1387) استفاده شد. معیارهای ضریب تبیین، ریشه میانگین مربعات خطا، میانگین قدر مطلق خطا و ضریب نش ساتکلیف برای ارزیابی و مقایسه مدلها مورد استفاده قرار گرفت. نتایج نشان داد ساختارهای ترکیبی نتایج قابل قبولی در مدلسازی دبی رودخانه ارائه می نمایند. مدل هیبریدی پیشنهادی ماشین بردار پشتیبان-گیاهان مصنوعی با ضریب همبستگی (985/0-933/0R=)، ریشه میانگین مربعات خطا ( m3/s088/0-008/0RMSE=)، میانگین قدرمطلق خطا ( m3/s040/0-004/0MAE= ) و ضریب نش ساتکلیف (995/0-951/0NS=) عملکرد بهتری در تخمین جریان داشته و می‏تواند در زمینه پیش بینی دبی روزانه رودخانه ها مفید باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Aplication of the Hybrid Model of Support Vector Machine-Algorithm Artificial Flora in Estimating the Daily Flow of Rivers (Case study: Dez basin)

نویسندگان [English]

  • Reza Dehghani 1
  • Hassan Torabi poudeh 2
  • Hojatolah Younesi 3
  • Babak SHahinejad 3
1 Ph.D. Student of Water Structure, Faculty of Agriculture, Lorestan University, Iran.
2 Associate Professor, Department of Water Engineering, Lorestan University
3 Assistant Professor, Department of Water Engineering, Lorestan University, Iran.
چکیده [English]

River flow prediction is one of the key issues in the management and planning of water resources, in particular the adoption of proper decisions in the event of floods and droughts. To predict the flow rate of rivers, various approaches have been introduced in hydrology, the most important of which are the intelligent models. In this study, a hybrid artificial flora- support vector machine model was applied to estimate the discharge of Dez Basin based on the daily discharge statistics provided by the hydrometric stations located at the upstream of the dam during the statistical period (2008-2018) and its performance was compared with the wavelet-support vector machine model. The correlation coefficients, root mean square error, and mean absolute error was used for evaluation and a comparison of the performance of models. The results showed that the hybrid structures presented acceptable outcomes in the modeling of river discharge. A comparison of models also showed that the hybrid model correlation coefficient (R= 0.933-0.985), root-mean-square error (RMSE = 0.008-0.088 m3/s), mean absolute error (MAE=0.008-0.088 m3/s) and the Nash–Sutcliffe coefficient (NS=0.951-0.995) has had better performance in estimating the flow. The results of the study of the charts disclosed that the suggested hybrid model has a suitable performance in estimating the minimum and maximum points and has fewer error in all selected stations.

کلیدواژه‌ها [English]

  • Artificial Flora Alghorithm
  • prediction
  • DEZ BASIN
  • Support Vector Machine
  1. Adnan R, Liang Z, Heddam S, Kermani M, Kisi O, Li B (2019) Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. Journal of Hydrology 19(4):432-448
  2. Alizadeh F, Gharamaleki A, Jalilzadeh M, Akhoundzadeh A (2020) Prediction of river stage-discharge process based on a conceptual model using EEMD-WT-LSSVM approach. Water Resources 47:41-53
  3. Basak D, Pal  S, Patranabis  DC (2007)  Support vector regression.  Neural Information Processing 11:203-225
  4. Cartlidge JP, Bulloc  SG (2004) Combating coevolutionary disengagement by reducing parasite virulence. Evolutionary Computation 12(2):193-222
  5. Chen H, Zhu Y (2008) Optimization based on symbiotic multi-species coevolution. Journal on Applied Mathematics and Computation 22(3):179-194
  6. Cheng L, Wu X, Wang Y (2018) Artificial Flora (AF) optimization algorithm. Applied Science 329(8):2-22
  7. Edossa DC, Babel MS (2012) Forecasting hydrological droughts using artificial neural network  modeling  technique  South Africa. University of Pretoria, Proceedings of 16th SANCIAHS National  Hydrology  Symposium:1–10
  8. Ghorbani MA, Khatibi R, Geol A, Fazelifard MH, Azani A (2016) Modeling river discharge time series using support vector machine and artificial neural networks. Environmental Earth Sciences 75(4):675-685
  9. Ghorbani MA, Khatibi R, Karimi V, Yaseen ZM, Zounemat-Kermani M (2018) Learning from multiple models using artificial intelligence to improve model prediction accuracies: Application to river flows. Water Resources Management 32(13):4201-4215
  10. Hamel L (2009) Knowledge discovery with support vector machines. Hoboken, N.J. John Wiley
  11. Hillis WD (1990) Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D: Nonlinear Phenomena 42:228–234, [CrossRef]
  12. Huang S, Chang J, Huang Q, Chen Y (2014) Monthly streamflow prediction using modified emd-based support vector machine. Journal of Hydrology 511(4):764-775
  13. Kisi O, Karahan M, Sen Z (2006) River suspended sediment modeling using fuzzy logic approach. Hydrological Process 20(2):4351-4362
  14. Lin JY, Cheng CT, Chau KW (2006) Using support vector machines for long-term discharge prediction. Hydrolog Sciences Journal 51(3):599–612
  15. Liong SY, Sivapragasam C (2002) Flood stage forecasting with support vector machines.  Journal of the American Water Resources Association  38(4):173–186

16.   Misra D, Oommen T, Agarwa A, Mishra SK, Thompson AM (2009) Application and analysis of support vector machine based simulation for runoff and sediment yield. Biosystems Engineering 103(3):527–535

  1. Mohammadi K,  Eslami  HR, Dardashti SD (2005) Comparisonof r egression, ARIMA and ANN models for reservoir Inflow forecasting using snowmelt equivalent (A case study of Karaj). Journal of Agricultural Science and Technology 7:17–30
  2. Nagy H, Watanabe K, Hirano M (2002) Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulics Engineering 128(3):558-559
  3. Othman F, Naseri M (2011) Reservoir inflow forecasting using artificial neural network. International Journal of the Physical Sciences 6(3):434-440
  4. Pagie L, Mitchell MA (2002) Comparison of evolutionary and coevolutionary search. International Journal of Computational Intelligence and Application 2:53–69
  5. Rosin CD, Belew RK (1995) Methods for competitive co-evolution. Finding Opponents Worth Beating in Proceedings of the International Conference on Genetic Algorithms Pittsburgh, 373–381
  6. Sedighi F, Vafakhah M,  Javadi  MR (2016) Rainfall–runoff modeling using support vector machine in snow-affected watershed. Arabian Journal for Science and Engineering 41(10):4065-4076
  7. Seyedian M, Bagherpour M, Fathabadi A, Mohammadi A (2018) Runoff prediction using black and gray box models. Iranian Water Resources Research 14(5):204-219 (In Persian)
  8. Shahinejad B, Dehghani R (2018) Comparison of wavelet neural network models, support vector machine and gene expression programming in estimating the amount of oxygen dissolved in rivers. Iran-Water Resources Research 14(3):226-238 (In Persian)
  9. Shin S,  Kyung  D,  Lee  S,  Taik & Kim  J, Hyun J  (2005)  An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications 28(4):127-135
  10. Taylor E (2001) Summarizing multiple aspects of model performance in a single diagram. Journal of Geophyysical Research 106(7):7183-7192
  11. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York
  12. Vapnik VN (1998) Statistical learning theory. Wiley, New York
  13. Vapnik V, Chervonenkis A (1991) The necessary and sufficient conditions for consistency in the empirical risk minimization method. Pattern Recognition and Image Analysis 1(3):283-305
  14. Wang D, Safavi AA, Romagnoli JA (2000) Wavelet-based adaptive robust M-estimator for non-linear system identification. AIChE Journal 46(4):1607-1615
  15. Wiegand RP, Sarma J (2004) Spatial Embedding and loss of gradient in cooperative coevolutionary algorithms. In Proceedings of the International Conference on Parallel Problem Solving from Nature, Berlin Germany 43:912–921
  16. Williams N, Mitchell M (2005) Investigating the success of spatial coevolution. In Proceedings of the 7th Annual Conference on Genetic And Evolutionary Computation Washington 46:523–530
  17. Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of Hydrology 396(4):128–138
  18. Zhang G, Patuwo BE, Hu YM (1998) Forecasting with artificialneural networks: The state of the art. International Journal of Forecasting 14(1):35-62            
  19. Zhao X, Chen X, Xu Y, Xi D, Zhang Y, Zheng X (2017) An EMD-based chaotic least squares support vector machine hybrid model for annual runoff forecasting.  Water 9(3):140-153
  20. Zhu YM, Lu XX, Zhou Y (2007) Suspended sediment flux modeling with artificial neural network: An example of the longchuanjiang river in the upper yangtze catchment. Geomorphology 84(4):111-125