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Yıl 2019, Cilt: 2 Sayı: 3, 161 - 169, 01.07.2019

Hafez Ahmad

https://doi.org/10.3153/AR19014

Öz

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. (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-in-flow 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., Ayala-Orozco, B. (2014). A statistical assessment of population trends for data deficient Mexican amphibians. PeerJ, 2, E703. 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 spark-ignition 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

MACHINE LEARNING APPLICATIONS IN OCEANOGRAPHY

Yıl 2019, Cilt: 2 Sayı: 3, 161 - 169, 01.07.2019

Hafez Ahmad

https://doi.org/10.3153/AR19014

Öz

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.

Anahtar Kelimeler

Machine learning Oceanography Application Data driven

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. (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-in-flow 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., Ayala-Orozco, B. (2014). A statistical assessment of population trends for data deficient Mexican amphibians. PeerJ, 2, E703. 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 spark-ignition 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
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Deniz Mühendisliği (Diğer)
Bölüm Review Articles
Yazarlar

Hafez Ahmad 0000-0001-9490-9335

Yayımlanma Tarihi 1 Temmuz 2019
Gönderilme Tarihi 16 Haziran 2019
Yayımlandığı Sayı Yıl 2019Cilt: 2 Sayı: 3

Kaynak Göster

APA Ahmad, H. (2019). MACHINE LEARNING APPLICATIONS IN OCEANOGRAPHY. Aquatic Research, 2(3), 161-169. https://doi.org/10.3153/AR19014
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