Port wave data simulation in Zarabad using Mike21 and data modification with artificial neural network

Document Type : Research Article

Authors

1 M.Sc. Student of Hydraulic Structures Civil Engineering, University of Sistan and Baluchistan, Sistan and Baluchistan, Iran

2 Assistant Professor, Institute of Geophysics, University of Tehran, Iran

3 Professor, Department of Civil Engineering, University of Sistan and Baluchistan, Sistan and Baluchistan, Iran

4 Associate Professor, Department of Civil Engineering, University of Sistan and Baluchistan, Sistan and Baluchistan, Iran

Abstract

Longshore sediment transport is considered as one of the most important and influential factors in the functioning of coastal areas. Forecasting and determining the rate of this parameter along the coast and in the vicinity of coastal structures is one the most important for shoreline management during any construction and coastal management mission. This study aims to put different pieces of knowledge together, including field measurements, neural networks, and numerical modelling to obtain a more realistic estimation of the LST rate along the undeveloped Makran Coastline. The focus of this paper is mostly on accurate wave and sediment transport modelling, verified against available field data and morphological evidence. A neural network for the correction of wave data and a numerical model of Mike21 is applied for simulating the transportation process of sediments along the Makran Coastline. Zarabad port on the coast of Makran has selected the case study of this research. The results of this study showed that the observed data are not in good agreement with the output data of the model (except wave height) and need to be modified with the neural network and considering the effective parameters such as the different conditions of the waves in the monsoon and
non-monsoon seasons in the Neural network. Neural network can help a lot in improving and correcting data. The results also showed that the sediment transport process occurs in different directions and depending on the wave height at depths more than 4m and at greater depths the sediment transport is insignificant, also, wave force in the region is able to transfer approximately 235000 m3 / year sediment.

Keywords

References

دهقانی، ر.، شاهی نژاد ب.، 1396، ارزیابی و عملکرد مدل ماشین بردار پشتیبان در تخمین رسوبات معلق رودخانه‌ها: نشریه آبیاری و آب ایران، دوره 8 شماره 1, صفحات 30-42.
Arz, H.W., Lamy, F., Patzold, J., Muller, P.J., Prins, M., 2003, Mediterranean moisture source for an early-Holocene humid period in the northern Red Sea: Science 300, 118-121.
DHI Water an Environment User Guide of MIKE21, 2007.
Dibajnia, M., Soltanpour, M., Vafai, F., Shoushtari, S.M.H.J., Kebriaee, A., 2012, A shoreline management plan for Iranian coastlines: Ocean & coastal management, 63, 1-15.
Evan, A.T., Camargo, S.J., 2011, A climatology of Arabian Sea cyclonic storms: Journal of climate 24, 140-158.
Haghshenas, S., Razavi, A., 2014, Applying a Combination of Numerical Simulations, Tracing Sediment Indicators and Satellite Imagery Analysis to Investigate Morphodynamic Changes in Mond Coastal Area: the Persian Gulf Proceedings of the 17th Physics of Estuaries and Coastal Seas (PECS) conference, Porto de Galinhas, Pernambuco, Brazil, 19, 24.
Haghshenas, S.A., Razavi Arab, A., Bakhtiari, A., Jedari Attari, M., Risk, M.J., 2016, Decadal Evolution of Mond River Delta: the Persian Gulf. Journal of Coastal Research, 805-809.
Krishna, K.M., 2009, Intensifying tropical cyclones over the North Indian Ocean during summer monsoon—Global warming: Global and Planetary Change 65, 12-16.
Larson, M., Kraus, N.C., Sunamura, T., 1988, Beach profile change: Morphology, transport rate, and numerical simulation: Coastal Engineering Proceedings, 96-96.
Law, Y., Santo, H., Lim, K., Chan, E., 2020, Deterministic wave prediction for unidirectional sea-states in real-time using Artificial Neural Network: Ocean Engineering 195, 106722.
Oo, Y.H., Zhang, H., 2022, Spatial wave assimilation by integration of artificial neural network and numerical wave model: Ocean Engineering 247, 110752.
PGC and DNP, 2018, Field Measurements Report of the 6th Phase of Monitoring and Modeling Studies of Iranian Coastline: Ports and Maritime Organization (PMO).
PGC and MEWE, 2018, 30-Year Wave Climate Hindcast for the Makran Coasts: In. Middle East Water and Environment Consulting Engineers Company (MEWE).
Shah-hosseini, M., Morhange, C., Beni, A.N., Marriner, N., Lahijani, H., Hamzeh, M., Sabatier, F., 2011, Coastal boulders as evidence for high-energy waves on the Iranian coast of Makran: Marine Geology 290, 17-28.
Iranian Journal of Geophysics
Volume 17, Issue 2
June 2023
Pages 139-155

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