Trajectories modelling of mesoscale anticyclonic eddies in the Mozambique Channel using ANFIS Fuzzy C-Means

Hanitra Elisa Rasoavololoniaina

¹ Laboratory of Atmosphere, Climate and Ocean Dynamics, University of Antananarivo, Science and Technology, Antananarivo, Madagascar

Email: ^a.[email protected]; ^b.[email protected]; ^c.[email protected]; ^d.[email protected]

*Corresponding Author: Hanitra Elisa Rasoavololoniaina, Email: [email protected]

The aim of this paper is to optimize the Fuzzy C-Means (FCM) model of the ANFIS neuro-fuzzy system to model the four types of mesoscale anticyclonic eddy trajectories in the Mozambique Channel as a function of the variables eddy speed average of contour, amplitude and diameter, horizontal wind, atmospheric pressure and bathymetry. The study area concerns the eastern part of the Mozambique Channel between longitudes 41°E-44°E and latitudes 16°S-25°S. We classified the eddy trajectories of interest in our study area into four types according to their formation and dissipation zones.  The data used are from the mesoscale eddy track atlas product derived from the META3 altimetry version. 1exp DT allsat for trajectories and eddy properties (amplitude, eddy rotation speed and diameter), GEBCO_2022 grid data for bathymetry, ECMWF data at spatial resolution 1° x 1° for atmospheric pressure, and Copernicus Marine data at spatial resolution 0.25° x 0.25° for wind. The latitudes and longitudes of the daily eddy displacement points from their formation to their dissipation characterize the trajectories. We used two different approaches in our study. The first approach consist to put each endogenous variable as input for the FCM model, while the second approach utilized the endogenous variables multiplied by the multiple regression coefficients. The results conclude that the case where the input variables of the model are preprocessed by the multiple (linear or polynomial) regression operation before FCM modeling is the best approach.

Neuro-fuzzy modelling, mesoscale anticyclonic eddy, Mozambique Channel, ANFIS, Fuzzy C-Means, multiple regression

Hanitra Elisa Rasoavololoniaina, Harimino Andriamalala Rajaonarisoa, Todihasina Roselin Randrianantenaina, Adolphe Andriamanga Ratiarison (2023). Trajectories modelling of mesoscale anticyclonic eddies in the Mozambique Channel using ANFIS Fuzzy C-Means. Journal of Artificial Intelligence and Systems, 5, 58–78. https://doi.org/10.33969/AIS.2023050105.

[1] TEINTURIER, S. (2008) Dynamique et stabilité de tourbillons océaniques en interaction avec la côte et la topographie, in ENS / Polytechnique - Laboratoire de Météorologie Dynamique École Nationale Supérieure de Techniques Avancées p. 210.

[2] AGUEDJOU, H.M.A. (2022) Caractéristiques des tourbillons de mésoéchelle dans locéan Atlantique tropical et leurs interactions avec latmosphère., in SDU2E - Sciences de l'Univers, de l'Environnement et de l'Espace Université d’Abomey-Calavi (UAC), BENIN-Cotutelle internationale Université Toulouse 3 - Paul Sabatier.

[3] Zhang, Z., et al. (2013) Universal structure of mesoscale eddies in the ocean. Geophysical Research Letters. 40. https://doi.org/10.1002/grl.50736

[4] Zhang, Z., W. Wang, and B. Qiu. (2014) Oceanic mass transport by mesoscale eddies. Science. 345(6194): p. 322-324. https://doi.org/10.1126/science.1252418

[5] Zhang, Z., et al. (2022) Coherence of Eddy Kinetic Energy Variation during Eddy Life Span to Low-Frequency Ageostrophic Energy. Remote Sensing. 14(15): p. 3793. https://doi.org/10.3390/rs14020379

[6] Aiki, H., X. Zhai, and R. Greatbatch. (2015) ENERGETICS OF THE GLOBAL OCEAN: THE ROLE OF MESOSCALE EDDIES. https://doi.org/10.1142/9789814696623_0004

[7] Chen, R., G.R. Flierl, and C. Wunsch. (2014) A Description of Local and Nonlocal Eddy–Mean Flow Interaction in a Global Eddy-Permitting State Estimate. Journal of Physical Oceanography. 44(9): p. 2336-2352. https://doi.org/10.1175/JPO-D-14-0009.1

[8] Richardson, P.L. (1983) Eddy kinetic energy in the North Atlantic from surface drifters. Journal of Geophysical Research: Oceans. 88(C7): p. 4355-4367. https://doi.org/10.1029/JC088iC07p04355

[9] Dimarco, S., et al. (2002) Volume transport and property distribution of the Mozambique Channel. Deep Sea Research Part II: Topical Studies in Oceanography. 49: p. 1481-1511. https://doi.org/10.1016/S0967-0645(01)00159-X

[10] Tew Kai, E. (2009) Rôle des tourbillons méso-échelle dans la structuration spatiale de l'écosystème pélagique : le cas du canal de Mozambique Role of mesoscale eddies in the spatial structuring of the pelagic ecosystem: Case of the Mozambique Channel.

[11] Twekai, E. and F. Marsac. (2010) Tourbillons méso-échelle et top prédateurs dans le canal de Mozambique. Information spatiale pour l'environnement et les territoires. 9: p. 44.

[12] Han, G., et al. (2019) SST Anomalies in the Mozambique Channel Using Remote Sensing and Numerical Modeling Data. Remote Sensing. 11(9): p. 1112. https://doi.org/10.3390/rs11091112

[13] Tristan COLOMA, Q.R.F.Y. (2022) Le canal du Mozambique : un espace de compétition crisogène.

[14] LINFO. (2018) Canal du Mozambique : un cargo sombre avec 8 000 litres de fuel au large de Madagascar, in LINFO.RE.

[15] LINFO. (2014) Belo sur Tsiribihina : le naufrage d’un boutre fait trois morts, in LINFO.RE.

[16] LINFO. (2015) Madagascar : 5 morts dans un naufrage d’une vedette à Majunga, in LINFO.RE.

[17] Roullet, G., et Patrice Klein. (2011) La turbulence océanique de méso-échelle, in Le climat à découvert, CNRS, Editor.

[18] AVISO. MESOSCALE EDDY TRAJECTORY ATLAS PRODUCT META3.1EXP DT.  [cited 2020; Available from: https://www.aviso.altimetry.fr/en/data/products/value-added-products/global-mesoscale-eddy-trajectory-product/meta3-1-exp-dt.html.

[19] Marine, C. Global Ocean Physics Reanalysis.  [cited 2020; Available from: https://data.marine.copernicus.eu/products.

[20] Jang, J.-S. (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics. 23(3): p. 665-685. https://doi.org/10.1109/21.256541

[21] Esmaili, M., et al. (2021) Assessment of adaptive neuro-fuzzy inference system (ANFIS) to predict production and water productivity of lettuce in response to different light intensities and CO2 concentrations. Agricultural Water Management. 258: p. 107201.

[22] Yeom, C.-U. and K.-C. Kwak. (2018) Performance Comparison of ANFIS Models by Input Space Partitioning Methods. Symmetry. 10: p. 700. https://doi.org/10.3390/sym10120700

[23] Adedeji, P.A., et al. (2020) Wind turbine power output very short-term forecast: a comparative study of data clustering techniques in a PSO-ANFIS model. Journal of Cleaner Production. 254: p. 120135. https://doi.org/10.1016/j.jclepro.2020.120135

[24] Bezzini, A. (2008) Commande Prédictive Non Linéaire en Utilisant Les Systèmes Neuro-Flous et les Algorithmes Génétiques.

[25] Adedeji, P., N. Madushele, and S. Akinlabi. (2018) Adaptive Neuro-fuzzy Inference System (ANFIS) for a multi-campus institution energy consumption forecast in South Africa. in Proceedings of the International Conference on Industrial Engineering and Operations Management Washington DC, USA, September 27-29 of Conference.

[26] Bezdek, J. (1981) Pattern Recognition With Fuzzy Objective Function Algorithms. https://doi.org/10.1007/978-1-4757-0450-1

[27] Bezdek, J.C. (1973) FUZZY-MATHEMATICS IN PATTERN CLASSIFICATION. Cornell University.

[28] Abdulshahed, A., A. Longstaff, and S. Fletcher. (2014) Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera. Applied Mathematical Modelling. https://doi.org/10.1016/j.apm.2014.10.016

[29] Oladipo, S., Y. Sun, and A. Amole. (2022) Performance Evaluation of the Impact of Clustering Methods and Parameters on Adaptive Neuro-Fuzzy Inference System Models for Electricity Consumption Prediction during COVID-19. Energies. 15(21): p. 7863.

[30] Abdulshahed, A.M., A.P. Longstaff, and S. Fletcher. (2015) The application of ANFIS prediction models for thermal error compensation on CNC machine tools. Applied soft computing. 27: p. 158-168. https://doi.org/10.1016/j.asoc.2014.11.012

[31] Laffly, D. (2006) Régression multiple : principes et exemples d’application. Available from: https://www.scribd.com/document/76603425/Laffly-Regression-Multiple.

[32] Cornillon, P.A. and E. Matzner-Lober. (2012) Régression avec R. Springer Paris. https://doi.org/10.1007/978-2-8178-0184-1

[33] Kim, H.-S., et al. (2011) Pattern Classification of Typhoon Tracks Using the Fuzzy c-Means Clustering Method. Journal of Climate. 24(2): p. 488-508. https://doi.org/10.1175/2010JCLI3751.1

[34] Gokten, S., P. Gokten, and F. Başer. (2017) Using fuzzy c-means clustering algorithm in financial health scoring. Audit Financiar Journal. 15: p. 385-394. https://doi.org/10.20869/AUDITF/2017/147/385

[35] Zhang, T., et al. (2022) Statistical Analysis of Mesoscale Eddies Entering the Continental Shelf of the Northern South China Sea. Journal of Marine Science and Engineering. 10(2): p. 206. https://doi.org/10.3390/jmse10020206