Machine learning methods applied to sea level predictions in the upper part of a tidal estuary

• Autorzy: Guillou Nicolas , Chapalain Georges

Identyfikatory

DOI

10.1016/j.oceano.2021.07.003

• Języki publikacji: EN

Abstrakty

EN

Sea levels variations in the upper part of estuary are traditionally approached by relying on refined numerical simulations with high computational cost. As an alternative efficient and rapid solution, we assessed here the performances of two types of machine learning algorithms: (i) multiple regression methods based on linear and polynomial regression functions, and (ii) an artificial neural network, the multilayer perceptron. These algorithms were applied to three-year observations of sea levels maxima during high tides in the city of Landerneau, in the upper part of the Elorn estuary (western Brittany, France). Four input variables were considered in relation to tidal and coastal surge effects on sea level: the French tidal coefficient, the atmospheric pressure, the wind velocity and the river discharge. Whereas a part of these input variables derived from large-scale models with coarse spatial resolutions, the different algorithms showed good performances in this local environment, thus being able to capture sea level temporal variations at semi-diurnal and spring-neap time scales. Predictions improved furthermore the assessment of inundation events based so far on the exploitation of observations or numerical simulations in the downstream part of the estuary. Results obtained exhibited finally the weak influences of wind and river discharges on inundation events.

Słowa kluczowe

EN

multiple regression methods; artificial neural network; multilayer perceptron; Elorn; Landerneau; Western Brittany

• Wydawca: Polish Academy of Sciences, Institute of Oceanology ; Elsevier
• Czasopismo: Oceanologia
• Rocznik: 2021
• Tom: No. 63 (4)
• Strony: 531--544
• Opis fizyczny: Bibliogr. 40 poz., rys., tab., wykr.

Twórcy

autor

Guillou Nicolas

• Laboratoire de Génie Côtier et Environnement (LGCE), Cerema, Plouzané, France

autor

Chapalain Georges

• Laboratoire de Génie Côtier et Environnement (LGCE), Cerema, Plouzané, France

Bibliografia

• 1. Ali, M., Prasad, R., Xiang, Y., Deo, R.C., 2020. Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms. Renew. Sust. Ener. Rev. 132, 110003. https://doi.org/10.1016/j.rser.2020.110003
• 2. Asma, S., Sezer, A., Ozdemir, O., 2012. MLR and ANN models of significant wave height on the west coast of India. Computers & Geosciences 49, 231-237. https://doi.org/10.1016/j.cageo.2012.05.032
• 3. Azencott, C.A., 2019. Introduction au Machine Learning. Dunod Dunod, Cambridge, UK, 240 pp.
• 4. Banque Hydro, 2021. Banque Hydro. http://hydro.eaufrance.fr/indexd.php (accessed on 03/2021).
• 5. Beudin, A., Chapalain, G., Guillou, N., 2014. Modelling dynamics and exchanges of fine sediments in the bay of Brest. La Houille Blanche 6, 47-53. https://doi.org/10.1051/lhb/2014062
• 6. Beuzen, T., Splinter, K., 2020. chapter Machine learning and coastal processes. Elsevier, 689-710. https://doi.org/10.1016/B978-0-08-102927-5.00028-X
• 7. Cerema-LGCE, 2021. https://www.cerema.fr/fr/cerema/directions/cerema- eau- mer- fleuves/lgce (accessed on 03/2021).
• 8. Chao, W.T., Young, C.C., Hsu, T.W., Liu, W.C., Liu, C.Y., 2020. Long-lead-time prediction of storm surge using artificial neural net-works and effective typhoon parameters: Revisit and deeper in-sight. Water 12. https://doi.org/10.3390/w12092394
• 9. Deo, M., Jha, A., Chaphekar, A., Ravikant, K., 2001. Neural networks for wave forecasting. Ocean Eng. 28, 889-898. https://doi.org/10.1016/S0029-8018(00)00027-5
• 10. Fan, S., Xiao, N., Dong, S., 2020. A novel model to predict significant wave height based on long short-term memory network. Ocean Eng. 205, 107298. https://doi.org/10.1016/j.oceaneng.2020.107298
• 11. Feng, X., Ma, G., Su, S.F., Huang, C., Boswell, M.K., Xue, P., 2020. A multi-layer perceptron approach for accelerated wave forecasting in Lake Michigan. Ocean Eng. 211, 107526. https://doi.org/10.1016/j.oceaneng.2020.107526
• 12. French, J., Mawdsley, R., Fujiyama, T., Achuthan, K., 2017. Combining machine learning with computational hydrodynamics for prediction of tidal surge inundation at estuarine ports. Procedia IUTAM 25, 28-35. https://doi.org/10.1016/j.piutam.2017.09.005
• 13. Hashemi, M.R., Spaulding, M.L., Shaw, A., Farhadi, H., Lewis, M., 2016. An efficient artificial intelligence model for prediction of tropical storm surge. Nat. Hazards 82, 471-491. https://doi.org/10.1007/s11069-016-2193-4
• 14. Humphrey, G.B., Gibbs, M.S., Dandy, G.C., Maier, H.R., 2016. A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a bayesian artificial neural network. J. Hydrol. 540, 623-640. https://doi.org/10.1016/j.jhydrol.2016.06.026
• 15. Keras, 2021. Keras, Simple. Flexible. Powerful. keras.io (accessed on 03/2021).
• 16. Kingma, D.P., Ba, J.L., 2015. Adam: A Method for Stochastic Optimization. International Conference on Learning Representations.
• 17. Kumar Paramasivan, S., 2019. Improved Prediction of Wind Speed using Machine Learning. EAI Endorsed Transactions on Energy Web 7. https://doi.org/10.4108/eai.13-7-2018.157033
• 18. Lee, T.L., Jeng, D.S., 2002. Application of artificial neural networks in tide-forecasting. Ocean Eng. 29, 1003-1022. https://doi.org/10.1016/S0029- 8018(01)00068- 3
• 19. Liu, J., Shi, G., Zhu, K., 2019. High-precision combined tidal forecasting model. Algorithms 12. https://doi.org/10.3390/a12030065
• 20. Manero, J., Béjar, J., Cortés, U., 2019. Deep Learning is blowing in the wind. Deep models applied to wind prediction at turbine level. IOP Conf. Series: J. Phys. 1222. https://doi.org/10.1088/1742-6596/1222/1/012037
• 21. Messikh, N., Bousba, S., Bougdah, N., 2017. The use of a multilayer perceptron (MLP) for modelling the phenol removal by emulsion liquid membrane. J. Environ. Chem. Eng. 5, 3483-3489. https://doi.org/10.1016/j.jece.2017.06.053
• 22. Nair, V., Hinton, G.E., 2010. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), 807-8014.
• 23. NOAA, 2021. Climate Forecast System (CFS). https://www.ncdc.noaa.gov/data- access/model- data/model- datasets (accessed on 03/2021).
• 24. Noori, N., Kalin, L., 2016. Coupling SWAT and ANN models for enhanced daily streamflow prediction. J. Hydrol. 533, 141-151. https://doi.org/10.1016/j.jhydrol.2015.11.050
• 25. Peng, M., Xie, L., Pietrafesa, L.J., 2004. A numerical study of storm surge and inundation in the Croatan-Albemarle-Pamlico Estuary System. Estuar. Coast. Shelf S. 59, 121-137. https://doi.org/10.1016/j.ecss.2003.07.010
• 26. Pinheiro, J.P., Lopes, C.L., Ribeiro, A.S., Sousa, M.C., Dias, J.M., 2020. Tide-surge interaction in Ria de Aveiro lagoon and its influence in local inundation patterns. Cont. Shelf Res. 200, 104132. https://doi.org/10.1016/j.csr.2020.104132
• 27. Renosh, P.R., Jourdin, F., Charantonis, A.A., Yala, K., Rivier, A., Badran, F., Thiria, S., Guillou, N., Leckler, F., Gohin, F., Garlan, T., 2017. Construction of multi-year time-series profiles of suspended particulate inorganic matter concentrations using machine learning approach. Remote Sens. 9. https://doi.org/10.3390/rs9121320
• 28. Rezaeianzadeh, M., Tabari, H., Yazdi, A., Isik, S., Kalin, L., 2013. Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput. Appl. 25. https://doi.org/10.1007/s00521-013-1443-6
• 29. Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.T., Chuang, H., Iredell, M., Ek, M.,Meng, J., Yang, R., Mendez, M.P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., Becker, E., 2014. The NCEP Climate Forecast System Version 2. J. Climate 27, 2185-2208. https://doi.org/10.1175/JCLI- D-12-00823.1
• 30. Sahoo, B., Bhaskaran, P.K., 2019. Prediction of storm surge and coastal inundation using Artificial Neural Network - A case study for 1999 Odisha Super Cyclone. Weather Clim. Extr. 23, 100196. https://doi.org/10.1016/j.wace.2019.100196
• 31. Shih, S.S., Kuo, P.H., Lai, J.S., 2019. A nonstructural flood prevention measure for mitigating urban inundation impacts along with river flooding effects. J. Environ. Manage. 251, 109553. https://doi.org/10.1016/j.jenvman.2019.109553
• 32. SHOM, 2021. https://www.data.shom.fr (accessed on 03/2021). Simon, B., 2007. La marée - La marée océanique côtière. Institut Océanographique, 433 pp.
• 33. Tréguer, P., Goberville, E., Barrier, N., L’Helguen, S., Morin, P.,Bozec, Y., Rimmelin-Maury, P., Czamanski, M., Grossteffan, E.,Cariou, T., Répécaud, M., Quéméner, L., 2014. Large and local-scale influences on physical and chemical characteristics of coastal waters of Western Europe during winter. J. Marine Syst. 139, 79-90. https://doi.org/10.1016/j.jmarsys.2014.05.019
• 34. Tsakiri, K., Marsellos, A., Kapetanakis, S., 2018. Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York. Water 10. https://doi.org/10.3390/w10091158
• 35. Wang, S., Mu, L., Liu, D., 2021a. A hybrid approach for El Niño prediction based on Empirical Mode Decomposition and convolutional LSTM Encoder-Decoder. Comput. Geosci. 149, 104695. https://doi.org/10.1016/j.cageo.2021.104695
• 36. Wang, Y., Chen, J., Cai, H., Yu, Q., Zhou, Z., 2021b. Predicting water turbidity in a macro-tidal coastal bay using machine learning approaches. Estuar. Coast Shelf S. 107276. https://doi.org/10.1016/j.ecss.2021.107276
• 37. Wang, Z.B., Vandenbruwaene, W., Taal, M., Winterwerp, H., 2019. Amplification and deformation of tidal wave in the Upper Scheldt Estuary. Ocean Dynam. 69, 829-839. https://doi.org/10.1007/s10236-019-01281- 3
• 38. WMO, 2021. World Meteorological Organization. Données d’observation des principales stations météorologiques https://www.data.gouv.fr/fr/datasets/donnees- d- observation- des-principales- stations- meteorologiques/ (accessed on 03/2021).
• 39. Wolff, S., O’Donncha, F., Chen, B., 2020. Statistical and machine learning ensemble modelling to forecast sea surface temperature. J. Marine Syst. 208, 103347. https://doi.org/10.1016/j.jmarsys.2020.103347
• 40. Wöppelmann, G., Pouvreau, N., Coulomb, A., Simon, B., Wood-worth, P.L., 2008. Tide gauge datum continuity at Brest wince 1711: France’s longest sea-level record. Geophys. Res. Lett. 35. https://doi.org/10.1007/s10236-005-0044-z

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).