MACHINE LEARNING APPLICATIONS IN OCEANOGRAPHY

Machine learning (ML) is a subset of artificial intelligence that enables to take decision based on data. Artificial intelligence makes possible to integrate ML capabilities into data driven modelling systems in order to bridge the gaps and lessen demands on human experts in oceanographic research .ML algorithms have proven to be a powerful tool for analysing oceanographic and climate data with high accuracy in efficient way. ML has a wide spectrum of real time applications in oceanography and Earth sciences. This study has explained in simple way the realistic uses and applications of major ML algorithms. The main application of machine learning in oceanography is prediction of ocean weather and climate, habitat modelling and distribution, species identification, coastal water monitoring, marine resources management, detection of oil spill and pollution and wave modelling.

Kaynakça

Bhattacharya, B., Solomatine, D.P. (2005). Neural networks and M5 model trees in modelling water level-discharge relationship. NeuroComputing, 63, 381-396. https://doi.org/10.1016/j.neucom.2004.04.016

Bhattacharya, B., Solomatine, D.P. (2006). Machine learning in sedimentation modelling. Neural Networks, 19(2), 208-214. https://doi.org/10.1016/j.neunet.2006.01.007

Boddy, L.M.C. (1999). Machine Learning Methods for Ecological Applications (p. 37-88 pp). Springer US, New York. https://doi.org/10.1007/978-1-4615-5289-5_2

Bolton, T., Zanna, L. (2019). Applications of deep learning to ocean data inference and subgrid parameterization. Journal of Advances in Modeling Earth Systems, 11(1), 376-399. https://doi.org/10.1029/2018MS001472

Brey, T., Jarre-Teichmann A.B.O. (1996). Artificial neural network versus multiple linear regression: Predicting P/B ratios from empirical data. Marine Ecology Progress Series, 140, 251-256. https://doi.org/10.3354/meps140251

Burkitt, A.N. (2006). A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input. Biological Cybernetics, 95(1), 1-19. https://doi.org/10.1007/s00422-006-0068-6

Hasan, R.C., Ierodiaconou, D., Monk, J. (2012). Evaluation of four supervised learning methods for benthic habitat mapping using backscatter from multi- beam sonar. Remote Sens, 4, 3427-3443. https://doi.org/10.3390/rs4113427

Cavasos, T., Comrie, A.C., Liverman, D.M. (2002). Intraseasonal Variability Associated with Wet Monsoons in Southeast Arizona, Journal of Climate, 15, 2477-490. https://doi.org/10.1175/1520- 0442(2002)015<2477:IVAWWM>2.0.CO;2

Diesing, M., Green, S.L., Stephens, D., Lark, R.M., Stewart, H.A., Dove, D. (2014). Mapping seabed sediments: Comparison of manual, geostatistical, object-based image analysis and machine learning approaches. Continental Shelf Research, 84, 107-119. https://doi.org/10.1016/j.csr.2014.05.004

Goldstein, E.B., Coco, G., Plant, N.G. (2018). A review of machine learning applications to coastal sediment transport and morphodynamics. https://doi.org/10.31223/osf.io/cgzvs

Del Frate, F., Petrocchi, A., Lichtenegger, J., Calabresi, G. (2000). Neural networks for oil spill detection Using ERS-SAR data. IEEE Transactions on Geoscience and Remote Sensing, 38(5), 2282-2287. https://doi.org/10.1109/36.868885

Forget, G., Campin, J., Heimbach, P., Hill, C.N., Ponte, R.M. (2015). ECCO version 4 : an integrated framework for non-linear inverse modeling and global ocean state estimation. Geoscientific Model Devolopment, 8, 3071- 3104. https://doi.org/10.5194/gmdd-8-3653-2015

Goodwin, J., North, E., Thompson, C.M. (2014). Evaluating and improving a semi-automated image analysis technique for identifying bivalve larvae. Limnology and Oceanography: Methods, 12, 548-562. https://doi.org/10.4319/lom.2014.12.548

Guo, Q., Kelly, M., Graham, C.H. (2005). Support vector machines for predicting distribution of Sudden Oak Death in California. Ecological Modelling, 182(1), 75-90. https://doi.org/10.1016/j.ecolmodel.2004.07.012

Haupt, S.E. (2009). Environmental Optimization: Applications of Genetic Algorithms. In: Haupt S.E., Pasini A., Marzban C. (eds) Artificial Intelligence Methods in the Environmental Sciences. Springer, Dordrecht, p. 379- 380. https://doi.org/10.1007/978-1-4020-9119-3_18

Headquarters, I. (2018). ICES WKMLEARN 2018 R EPORT Report of the Workshop on Machine Learning in Marine Science (WKMLEARN) International Council for the Exploration of the Sea, (April), 16-20.

Hollinger, G. A., Pereira, A., Ortenzi, V., Sukhatme, G. S. (2012). Towards Improved Prediction of Ocean Processes Using Statistical Machine Learning. In Robotics: Science and Systems Workshop on Robotics for Environmental Monitoring, Sydney, Australia, Jul 2012. http://robotics.usc.edu/publications/downloads/pub/775/ (accessed 23.12.2018)

Hsieh, W.W. (2009). Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels. Cambridge University Press. https://doi.org/10.1017/CBO9780511627217

James, S.C., Zhang, Y., O’Donncha, F. (2018). to Forecast Wave Conditions. Coastal Engineering, 137, 1-10. https://doi.org/10.1016/j.coastaleng.2018.03.004

Jennings, N., Parsons, S., Pocock, M.J.O. (2008). Human vs. machine: identification of bat species from their echolocation calls by humans and by artificial neural networks. Canadian Journal of Zoology, 86(5), 371-377. https://doi.org/10.1139/Z08-009

Horstmann, J., Schiller, H., Schulz-Stellenfleth, J., Lehner, S. (2003). Global wind speed retrieval from SAR. IEEE Transactions on Geoscience and Remote Sensing, 41(10), https://doi.org/10.1109/TGRS.2003.814658

Jones, M., Fielding, A., Sullivan, M. (2006). Analysing extinction risk in parrots using decision trees. Biodivers Conserv, 15(6), 1993-2007. https://doi.org/10.1007/s10531-005-4316-1

Kim, Y.H., Im, J., Ha, H.K., Choi, J.K., Ha, S. (2014). Machine learning approaches to coastal water quality monitoring using GOCI satellite data. GIScience and Remote Sensing, 51(2), 158-174. https://doi.org/10.1080/15481603.2014.900983

Krasnopolsky V.M. (2009) Neural Network Applications to Solve Forward and Inverse Problems in Atmospheric and Oceanic Satellite Remote Sensing. In: Haupt S.E., Pasini A., Marzban C. (eds) Artificial Intelligence Methods in the Environmental Sciences. Springer, Dordrecht. p. 191-205. https://doi.org/10.1007/978-1-4020-9119-3_9

Kubat, M., Holte, R.C., Matwin, S. (1998). Machine Learning for the detection of oil spills in satellite radar Images. Machine Learning, 30(2-3), 195-215. https://doi.org/10.1023/A:1007452223027

Lewis, J.M., Weinberger, K.Q., Saul, L.K. (2001). Mapping Uncharted Waters : Exploratory Analysis, Visualization, and Clustering of Oceanographic Data 2821 Mission College Blvd.

Mori, U., Mendiburu, A., Keogh, E., Lozano, J.A. (2017). Reliable early classification of time series based on discriminating the classes over time. Data Mining and Knowledge Discovery, 31(1), 233-263. https://doi.org/10.1007/s10618-016-0462-1

Múnera, S., Osorio, A.F., Velásquez, J.D. (2014). Data-based methods and algorithms for the analysis of sandbar behavior with exogenous variables. Computers and Geosciences, 72, 134-146. https://doi.org/10.1016/j.cageo.2014.07.009

Oja, E. (1982). Simplified neuron model as a principal component analyzer. Journal of Mathematical Biology, 15(3), 267-273. https://doi.org/10.1007/BF00275687

Olson, R.J., Sosik, H.M. (2007). Automated taxonomic classification of phytoplankton sampled with imaging-inflow cytometry. Limnology and Oceanography: Methods, 5, 204-216. https://doi.org/10.4319/lom.2007.5.204

O’Donncha, F. (2017). Using deep learning to forecast ocean waves. https://phys.org/news/2017-09-deep-ocean.html (accessed 23.12.2018)

Quintero, E., Thessen, A.E., Arias-Caballero, P., AyalaOrozco, B. (2014). A statistical assessment of population trends for data deficient Mexican amphibians. PeerJ, 2, https://doi.org/10.7717/peerj.703

Simmonds, J.E., Armstrong, F., Copland, P.J. (1996). Species identification using wideband backscatter with neural network and discriminant analysis. ICES Journal of Marine Science, 53(2), 189-195. https://doi.org/10.1006/jmsc.1996.0021

Tanaka, A., Kishino, M., Doerffer, R., Schiller, H., Oishi, T., Kubota, T. (2004). Development of a neural network algorithm for retrieving concentrations of chlorophyll, suspended matter and yellow substance from radiance data of the ocean color and temperature scanner, Journal of Oceanography, 60(3), 519-530. https://doi.org/10.1023/B:JOCE.0000038345.99050.c0

Thessen, A. (2016). Adoption of machine learning techniques in ecology and earth science. One Ecosystem, 1, e8621. https://doi.org/10.3897/oneeco.1.e8621

Tscherko D, Kandeler E, Bárdossy, A. (2007). Fuzzy classification of microbial biomass and enzyme activities in grassland soils. Soil Biology and Biochemistry, 39(7), 1799-1808. https://doi.org/10.1016/j.soilbio.2007.02.010

Turkson, R F., Yan, F., Ali, M.K.A., Hu, J. (2016). Artificial neural network applications in the calibration of sparkignition engines: An overview. Engineering Science and Technology, an International Journal, 19(3), 1346- 1359. https://doi.org/10.1016/j.jestch.2016.03.003

van Maanen, B., Coco, G., Bryan, K.R., Ruessink, B. G. (2010). The use of artificial neural networks to analyze and predict alongshore sediment transport. Nonlinear Processes in Geophysics, 17, 395-404. https://doi.org/10.5194/npg-17-395-2010

Wu, A., Hsieh, W.W., Tang, B. (2006). Neural network forecasts of the tropical Pacific sea surface temperatures. Neural Networks, 19(2), 145-154. https://doi.org/10.1016/j.neunet.2006.01.004

Yi, J., Prybutok, V.R. (1996). A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. Environmental Pollution, 92(3), 349-357. https://doi.org/10.1016/0269-7491(95)00078-X

Kaynak Göster