Intelligent Modeling of Salty Density Current in the Presence of Permeable Obstacles

Subject Areas : Subjects

Mehdi Derakhshannia ¹ (Ph.D. Candidate, Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.)
Mehdi Ghomeshi ² (Professor, Department of Water Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.)
Saied saeid Eslamian ³ (Professor, Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. and Department of Water Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran.)
Seyed Mahmood Kashefipour ⁴ (Professor, Department of Water Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.)

Received: 2020-10-01 Accepted : 2021-09-01 Published : 2022-07-23

Keywords: density current, sedimentation, Head reduction percentage, Feed Forward neural network, Multivariate regression,

Abstract :

Abstract
Introduction: Density current is one of the most important factors in the sedimentation process of dams. Increased sediment will reduce dam storage capacity and makes significant challenges for relevant engineers. Therefore, understanding the dynamics of density fluids and related sediment patterns is very efficient for dam reservoir management.
Methods: The purpose of this study was to create an intelligent model with appropriate adaptation to laboratory data so that, it can be used in future designs with different variables. Therefore, in this study, the percentage of reduction of density salt current head under the influence of trapezoidal permeable obstacles (aggregates with a diameter of 1 cm), taking into account variables such as discharge, slope, concentration and height of obstacles in laboratory.
Findings: Based on the results, the density salt current head was modeled using the artificial neural network feed-forward method and the classical multivariate regression method, and the performance of these two methods was compared. The results showed that the intelligent feed neural network intelligent method in modeling the percentage reduction of density salt current head is significantly superior to the multivariate regression method so that the training, calibration and test regression values ​​ are 0.99, 0.98 and 0.98 were obtained for neural network and 0.92, 0.91 and 0.91 for multivariate regression, respectively.
Conclusion: The performance of the artificial neural network is much better than the multivariate regression method.

References:

References

1. Abbaspour A, Farsadizadeh D, Ghorbani MA (2013) Estimation of hydraulic jump on corrugated bed using artificial neural networks and genetic programming. Water Sci Eng 6:189–198.


2. Abd El-Gawad SM, Pirmez C, Cantelli A, Minisini D, Sylvester Z, Imran J (2012) 3-D numerical simulation of turbidity currents in submarine canyons off the Niger Delta. Mar Geol 326–328:55–66.


3. Alexander J, Mulder T (2002) Experimental quasi-steady density currents. Mar Geol 186:195–210.


4. Asghari Pari, S. A., Kashefipour, S. M., Ghomeshi, M., & Bajestan, M. S. (2010). Effects of obstacle heights on controlling turbidity currents with different concentrations and discharges. Journal of Food, Agriculture and Environmen.,  8(2),  930–935.


5. Baas JH, Kesteren WV, Postma P (2004) Deposits of depletive high-density turbidity currents: a flume analogue of bed geometry, structure and texture. Sedimentology 51:1053–1088.


6. Baghalian S, Bonakdari H, Nazari F, Fazli M (2012) Closed-form solution for flow field in curved channels in comparison with experimental and numerical analyses and artificial neural network. Eng Appl Comput Fluid 6:514–526.


7. Baghalian, S., & Ghodsian, M. (2017). Experimental analysis and prediction of velocity profiles of turbidity current in a channel with abrupt slope using artificial neural network. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39(11), 4503-4517.


8. Belvederesi, C., Dominic, J. A., Hassan, Q. K., Gupta, A., and Achari, G. (2020). Predicting river flow using an AI-based sequential adaptive neuro-fuzzy inference system. Water, 12(6), 1622.


9. Bishop, C.M., 2006. Pattern recognition and machine learning. springer.


10. Brandt SA (1999) Reservoir distillation by means of hydraulic flushing. Ph.D. thesis, Inst. of Geog. Faculty of Sci. University of Copenhagen, pp 204.


11. Cai, Z., & Naruse, H. (2019). Application of Deep Learning Neural Network to the Inverse Analysis of Experimental Scale Turbidity Currents. In AGU Fall Meeting Abstracts (Vol. 2019, pp. EP53E-2290).


12. Chang, L. C., Chang, F. J., Kao, I. F., Chien, C. L., Lin, Y. C., Chang, C. J., ... & Yang, S. N. (2019). Building an Intelligent Reservoir Operation Decision Support System for Flood and Sedimentation Control. In 3rd International workshop on Sediment Bypass Tunnels.


13. Chang, M. J., Lin, G. F., Chen, P. A., Lee, F. Z., & Lai, J. S. (2020). Development of a real-time forecasting model for turbidity current arrival time to improve reservoir desilting operation. Hydrological Sciences Journal, 65(6), 1022-1035.


14. Fan J, Morris G (1992) Reservoir sedimentation. I: delta and density current. J Hydraul Eng 118:354–369.


15. Farizan, A., Yaghoubi, S., Firoozabadi, B., & Afshin, H. (2019). Effect of an obstacle on the depositional behaviour of turbidity currents. Journal of Hydraulic Research, 57(1), 75-89.


16. Felix M, Sturton S, Peakall J (2005) Combined measurements of velocity and concentration in experimental turbidity currents. Sediment Geol 179:31–47.


17. Firoozabadi B, Afshin H, Aram E (2009) Three-dimensional modeling of density current in a straight channel. J Hydraul Eng 135:393–402.


18. Garcia M (1993) Hydraulic jumps in sediment-driven bottom current. J Hydraul Eng 119:1094–1117.


19. Houichi L, Dechemi N, Heddam S, Achour B (2013) An evaluation of ANN methods for estimating the length of hydraulic jumps in U-shaped channel. J Hydroinform 15:147–154.


20. Kneller B (2003) ‘‘The influence of flow parameters on turbidite slope channel architecture. Mar Pet Geol 20(2003):901–910.


21. Kochenderfer, M.J. and Wheeler, T.A., 2019. Algorithms for optimization. Mit Press.


22. Mrutyunjaya S, Khatua KK, Mahapatra SS (2011) A neural network approach for prediction of discharge in straight compound open channel flow. Flow Meas Instrum 22:438–446.


23. Naruse, H., & Nakao, K. (2020). Inverse modeling of turbidity currents using artificial neural network: verification for field application. Earth Surface Dynamics Discussions, 1-27.


24. Nogueira HIS, Adduce C, Alves E, Franca MJ (2013) Analysis of lock-exchange gravity currents over smooth and rough beds. J Hydraul Res 51:417–431.


25. Peters WD, Venart JES (2000) Visualization of rough-surface gravity current flows using laser-induced fluorescence. In: 9th international symposium on flow visualization


26. Rojas, R., 2013. Neural networks: a systematic introduction. Springer Science & Business Media.


27. Song, C., & Zhang, H. (2020). Study on turbidity prediction method of reservoirs based on long short term memory neural network. Ecological Modelling, 432, 109210.


28. Yuhong Z, Wenxin H (2009) Application of artificial neural network to predict the friction factor of open channel flow. Commun Nonlinear Sci Numer Simul 14:2373–2378.