A comparative study of biological production in eastern boundary upwelling systems using an artificial neural network
暂无分享,去创建一个
[1] Robert H. Weisberg,et al. Patterns of ocean current variability on the West Florida Shelf using the self-organizing map , 2005 .
[2] H. Sverdrup,et al. On Conditions for the Vernal Blooming of Phytoplankton , 1953 .
[3] Judith E. Dayhoff,et al. Neural Network Architectures: An Introduction , 1989 .
[4] D. Pauly,et al. Primary production required to sustain global fisheries , 1995, Nature.
[5] James C. McWilliams,et al. Eddy-resolving simulation of plankton ecosystem dynamics in the California Current System , 2006 .
[6] Robert L. Smith,et al. Temporal variation observed in the hydrographic regime near Cabo Corveiro in the northwest African upwelling region, February to April 1974 , 1977 .
[7] R. Mendelssohn,et al. Common and uncommon trends in SST and wind stress in the California and Peru-Chile current systems , 2002 .
[8] A. Cuttitta,et al. Factors responsible for the differences in satellite-based chlorophyll a concentration between the major global upwelling areas , 2008 .
[9] Teuvo Kohonen,et al. Self-Organization and Associative Memory , 1988 .
[10] M. Ooe,et al. Ocean Tide Models Developed by Assimilating TOPEX/POSEIDON Altimeter Data into Hydrodynamical Model: A Global Model and a Regional Model around Japan , 2000 .
[11] P. Falkowski,et al. Role of eddy pumping in enhancing primary production in the ocean , 1991, Nature.
[12] P. Strutton,et al. Correction to “Iron links river runoff and shelf width to phytoplankton biomass along the U.S. West Coast” , 2007 .
[13] P. Marchesiello,et al. Eddy activity and mixing in upwelling systems: a comparative study of Northwest Africa and California regions , 2009 .
[14] John G. Field,et al. Identifying characteristic chlorophyll a profiles in the coastal domain using an artificial neural network , 2002 .
[15] Hervé Demarcq,et al. Coastal upwelling and associated retention indices derived from satellite SST. Application to Octopus vulgaris recruitment , 2000 .
[16] T. Platt,et al. An estimate of global primary production in the ocean from satellite radiometer data , 1995 .
[17] Robert H. Weisberg,et al. Ocean Currents and Sea Surface Heights Estimated Across the West Florida Shelf , 2007 .
[18] C. Provost,et al. Biophysical regions identification using an artificial neuronal network: A case study in the South Western Atlantic , 2006 .
[19] K. Brink. The near-surface dynamics of coastal upwelling , 1983 .
[20] P. Strutton,et al. Iron links river runoff and shelf width to phytoplankton biomass along the U.S. West Coast , 2007 .
[21] Young-Seuk Park,et al. Implementation of artificial neural networks in patterning and prediction of exergy in response to temporal dynamics of benthic macroinvertebrate communities in streams , 2001 .
[22] D. Volkov,et al. Improving the quality of satellite altimetry data over continental shelves , 2007 .
[23] Sampsa Laine,et al. Using SOM-Based Data Binning to Support Supervised Variable Selection , 2004, ICONIP.
[24] P. K. Kundu,et al. On the dynamics of the California current system , 1987 .
[25] K. Benabdeslem,et al. Feature Selection for Self-Organizing Map , 2007, 2007 29th International Conference on Information Technology Interfaces.
[26] Michael P. Meredith,et al. Circumpolar response of Southern Ocean eddy activity to a change in the Southern Annular Mode , 2006 .
[27] W. Fennel. Theory of the Benguela Upwelling System , 1999 .
[28] Eric D. Barton,et al. Variability in plankton community structure, metabolism, and vertical carbon fluxes along an upwelling filament (Cape Juby, NW Africa) , 2004 .
[29] Jacob Cohen,et al. Applied multiple regression/correlation analysis for the behavioral sciences , 1979 .
[30] Hiroshi Motoda,et al. Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.
[31] Sylvie Thiria,et al. Detecting decadal changes in ENSO using neural networks , 2006 .
[32] Nicolas Gruber,et al. What controls biological production in coastal upwelling systems? Insights from a comparative modeling study , 2011 .
[33] S. Lek,et al. Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters , 2003 .
[34] Edward J. Kearns,et al. Production regimes in four Eastern Boundary Current systems , 2003 .
[35] J. Austin,et al. The Inner Shelf Response to Wind-Driven Upwelling and Downwelling* , 2002 .
[36] Chuanmin Hu,et al. The importance of continental margins in the global carbon cycle , 2005 .
[37] S. Mulitza,et al. Rapid 20th-Century Increase in Coastal Upwelling off Northwest Africa , 2007, Science.
[38] Teuvo Kohonen,et al. Self-Organizing Maps , 2010 .
[39] A. Obata,et al. Global verification of critical depth theory for phytoplankton bloom with climatological in situ temperature and satellite ocean color data , 1996 .
[40] Robert L. Smith,et al. The physical environment of the Peruvian upwelling system , 1983 .
[41] Tereza Cavazos. Using Self-Organizing Maps to Investigate Extreme Climate Events: An Application to Wintertime Precipitation in the Balkans , 2000 .
[42] Richard B. Alley,et al. North Atlantic climate variability from a self-organizing map perspective , 2007 .
[43] E. Mittelstaedt. The upwelling area off Northwest Africa—A description of phenomena related to coastal upwelling , 1983 .
[44] R. Mendelssohn,et al. Increased coastal upwelling in the California Current System , 1997 .
[45] James C. McWilliams,et al. Eddy-induced reduction of biological production in eastern boundary upwelling systems , 2011 .
[46] Walter H. F. Smith,et al. Global Sea Floor Topography from Satellite Altimetry and Ship Depth Soundings , 1997 .
[47] M. Carr. Estimation of potential productivity in Eastern Boundary Currents using remote sensing , 2001 .
[48] J. Allen. Upwelling and Coastal Jets in a Continuously Stratified Ocean , 1973 .
[49] Robert Hallberg,et al. The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project , 2006 .
[50] Christopher N. K. Mooers,et al. Performance evaluation of the self‐organizing map for feature extraction , 2006 .
[51] V. Garçon,et al. Comparative study of mixing and biological activity of the Benguela and Canary upwelling systems , 2008 .
[52] Daniele Iudicone,et al. Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology , 2004 .
[53] Toulouse,et al. Surface mixing and biological activity in the four Eastern Boundary Upwelling Systems , 2009, 0909.0115.
[54] M. Meredith,et al. Eddy Heat Flux in the Southern Ocean: Response to Variable Wind Forcing , 2008 .
[55] R. Kudela,et al. Trends in primary production in the California Current detected with satellite data , 2009 .
[56] Aaron M. Ellison,et al. A Primer of Ecological Statistics , 2004 .
[57] Anthony J. Richardson,et al. Using self-organizing maps to identify patterns in satellite imagery , 2003 .
[58] Hiroshi Motoda,et al. Data Processing and Knowledge Discovery in Databases , 1998 .
[59] K. Schulten,et al. Kohonen's self-organizing maps: exploring their computational capabilities , 1988, IEEE 1988 International Conference on Neural Networks.
[60] A Bakun,et al. Global Climate Change and Intensification of Coastal Ocean Upwelling , 1990, Science.
[61] K. Cochrane,et al. The 1980s – a decade of change in the Benguela ecosystem , 1992 .
[62] Henry A. Miller,et al. A process-oriented modelling study of the coastal Canary and Iberian Current system , 2007 .
[63] T. D. Dickey,et al. Influence of mesoscale eddies on new production in the Sargasso Sea , 1998, Nature.
[64] Anil K. Jain,et al. Algorithms for Clustering Data , 1988 .
[65] Arijit Laha,et al. Detecting Topology Preserving Feature Subset with SOM , 2004, CIT.
[66] P Barbieri,et al. Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets. , 2007, Water research.
[67] Gianpiero Cossarini,et al. Understanding dynamic of biogeochemical properties in the northern Adriatic Sea by using self‐organizing maps and k‐means clustering , 2007 .
[68] Eric D. Barton,et al. The transition zone of the Canary Current upwelling region , 1998 .
[69] P. Falkowski,et al. Photosynthetic rates derived from satellite‐based chlorophyll concentration , 1997 .
[70] Ding-Geng Chen,et al. A neural network model for forecasting fish stock recruitment , 1999 .
[71] T. Platt,et al. Critical depth and marine primary production , 1991, Proceedings of the Royal Society of London. Series B: Biological Sciences.