Predicting the surface currents of the Strait of Hormuz using artificial neural network

Document Type : Original Article

Authors

1 Faculty of Marine Sciences and Oceanography, Khorramshahr university of Marine science and technology, Khorramshahr, Iran

2 Assistant professor , Faculty of Marine Sciences and Oceanography, Khorramshahr university of Marine science and technology, Khorramshahr, Iran

3 Faculty of Marine Sciences, University of Mazandaran, Babolsar, Iran

Abstract

Sea current velocity measurement plays an important role in engineering design and measurements. Studies in the Persian Gulf and the Strait of Hormuz have conducted field studies or numerical modeling of flows in this region. In the present research, the surface currents of the Strait of Hormuz are predicted using artificial neural network approaches. In order to determine the model’s inputs, the eastern and western time series of surface currents of the strait are used, and the basins affecting the currents of this strait are identified using linear regression model. Then the neural network inputs are defined in two different cases. The first case of the time series of known basins is considered as the input of the neural network. In the other case, combinations of known time series are considered as the input of the neural network using the Jones schema. By comparing these two cases, it is concluded that the neural network model using the Jones scheme has a good performance in predicting the surface currents of this strait. In order to further investigate the neural network model, the flow data is divided into 16 different categories; so that in each category the difference between the minimum and maximum speed is 0.03 and the average of each category is considered as the output of the neural network. To determine the network inputs, the same is done for the two cases previously mentioned. In this case, the results show that the neural network model predicts surface currents with an accuracy of R = 0.85.

Keywords

Main Subjects

References

[1] گلوردی عیسی. جغرافیای جزایر ایرانی خلیج فارس. سازمان جغرافیایی نیروهای مسلح؛ 1381.
[2] Chao Yu, Timothy Kao, Khalid R, AlHajri k. A numerical investigation of circulation in the Persian Gulf. Journal of Geophysical Research.1992: 11219-11236.
[3] Reynolds RM. Physical oceanography of the Gulf, Strait of Hormuz, and the Gulf of Oman—Results from the Mt Mitchell expedition. Marine Pollution Bulletin. 1993 Jan 1;27:35-59.
[4] Thoppil PG, Hogan PJ. A modeling study of circulation and eddies in the Persian Gulf. Journal of Physical Oceanography. 2010 Sep;40(9):2122-34.
[5] Sadrinasab M, Kämpf J. Three dimensional flushing times of the Persian Gulf. Geophysical research letters. 2004 Dec;31(24).
[6] Alosairi Y, Pokavanich T, Alsulaiman N. Three-dimensional hydrodynamic modelling study of reverse estuarine circulation: Kuwait Bay. Marine pollution bulletin. 2018 Feb 1;127:82-96.
[7] Aldababseh A, Temimi M. Analysis of the long-term variability of poor visibility events in the UAE and the link with climate dynamics. Atmosphere. 2017 Dec;8(12):242.
[8] Ling J, Kurzawski A, Templeton J. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. Journal of Fluid Mechanics. 2016 Nov;807:155-66.
[9] Lguensat R, Miao S, Ronan F, Pierre T, Evan M, Chen Ge. EddyNet: A deep neural network for pixel-wise classification of oceanic eddies. International Geoscience and Remote Sensing Symposium. 2018: 1764-7.
[10] Chapman C, Charantonis AA. Reconstruction of subsurface velocities from satellite observations using iterative self-organizing maps. IEEE Geoscience and Remote Sensing Letters. 2017 Mar 14;14(5):617-20.
[11] Bolton T, Zanna L. Applications of deep learning to ocean data inference and subgrid parameterization. Journal of Advances in Modeling Earth Systems. 2019 Jan;11(1):376-99.
[12] Bar-Sinai Y, Hoyer S, Hickey J, Brenner MP. Data-driven discretization: a method for systematic coarse graining of partial differential equations. arXiv preprint arXiv:1808.04930. 2018.
[13] Pathak J, Hunt B, Girvan M, Lu Z, Ott E. Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach. Physical review letters. 2018 Jan 12;120(2):024102.
[14] Zanna L, Bolton T. Data driven equation discovery of ocean mesoscale closures. Geophysical Research Letters. 2020 Sep 16;47(17):e2020 GL088376.
[15] Gentine P, Pritchard M, Rasp S, Reinaudi G, Yacalis G. Could machine learning break the convection parameterization deadlock?. Geophysical Research Letters. 2018 Jun 16;45(11):5742-51.
[16] Bolton T, Zanna L. Applications of deep learning to ocean data inference and subgrid parameterization. Journal of Advances in Modeling Earth Systems. 2019 Jan;11(1):376-99.
[17] Sinha A, Abernathey R. Estimating Ocean Surface Currents with Machine Learning. Frontiers in Marine Science. 2021 Jun 9.
[18] Johns WE, Yao F, Olson DB, Josey SA, Grist JP, Smeed DA. Observations of seasonal exchange through the Straits of Hormuz and the inferred heat and freshwater budgets of the Persian Gulf. Journal of Geophysical Research: Oceans. 2003 Dec;108 (C12).
[19] Vieira F, Cavalcante G, Campos E, Taveira-Pinto F. A methodology for data gap filling in wave records using Artificial Neural Networks. Applied Ocean Research. 2020 May 1;98:102109.
[20] van Gent MR, van den Boogaard HF, Pozueta B, Medina JR. Neural network modelling of wave overtopping at coastal structures. Coastal engineering. 2007 Aug 1;54(8):586-93.
[21] Beale MH, Hagan MT, Demuth HB. Deep Learning Toolbox—User's Guide, R2019a. Tech. Rep. MathWorks, Inc.. 2019.
[22] Levenberg K. A method for the solution of certain non-linear problems in least squares. Quarterly of applied mathematics. 1944;2(2):164-8.
[23] NASA. ocean motion and surface current [Internet]. 2015. [cited 2015 Nov]. Available from: http://oceanmotion.org

Files

Share

How to cite

Statistics