Relating sardine recruitment in the Northern Benguela to satellite-derived sea surface height using a neural network pattern recognition approach

Hardman-Mountford, NJ; Richardson, Anthony J.; Boyer, D. C.; Kreiner, Anja; Boyer, Helen

doi:10.1016/j.pocean.2003.07.005

Cited by 46 publications

(28 citation statements)

References 34 publications

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“…Hyun et al (2005) used the SOM to pattern temporal variations in longterm fisheries data according to the 30 commercially most important species; five clusters were identified corresponding to different time periods reflecting environmental and economic forcings on fish catch. Other SOM applications include the study of the genetic population structure of a sturgeon species (Zhu et al, 2006) and the identification of characteristic patterns from sea level differences using a seven-year time series of satellitederived data (Hardman-Mountford et al, 2003;Fig. 4).…”

Section: Self-organizing Map (Som)mentioning

confidence: 99%

The Use of Artificial Neural Networks (ANNs) in Aquatic Ecology

Quetglas¹,

Ordines²,

Guijarro³

2011

Artificial Neural Networks - Application

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Section: Self-organizing Map (Som)mentioning

confidence: 99%

The Use of Artificial Neural Networks (ANNs) in Aquatic Ecology

Quetglas¹,

Ordines²,

Guijarro³

2011

Artificial Neural Networks - Application

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“…For example, Ainsworth (1999) and Ainsworth and Jones (1999) used this method to improve chlorophyll estimates from satellite data. Silulwane et al (2001) and Richardson et al (2002) used it to identify characteristic chlorophyll profiles in the ocean, and Hardman- Mountford et al (2003) applied this method to altimeter data. Ultsch and Röske (2002) used it to predict sea level.…”

Section: A Self-organizing Map and Its Applications In Meteorology Amentioning

confidence: 99%

“…Ultsch and Röske (2002) used it to predict sea level. The SOM technique was also used to extract SST and wind patterns from satellite data (Richardson et al 2003;Risien et al 2004), and to detect ocean current spatial patterns from moored velocity time series (Liu and Weisberg 2005b). …”

Section: A Self-organizing Map and Its Applications In Meteorology Amentioning

confidence: 99%

Sea Surface Temperature Patterns on the West Florida Shelf Using Growing Hierarchical Self-Organizing Maps

Liu

Weisberg

2006

Journal of Atmospheric and Oceanic Technology

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Neural network analyses based on the self-organizing map (SOM) and the growing hierarchical selforganizing map (GHSOM) are used to examine patterns of the sea surface temperature (SST) variability on the West Florida Shelf from time series of daily SST maps from 1998 to 2002. Four characteristic SST patterns are extracted in the first-layer GHSOM array: winter and summer season patterns, and two transitional patterns. Three of them are further expanded in the second layer, yielding more detailed structures in these seasons. The winter pattern is one of low SST, with isotherms aligned approximately along isobaths. The summer pattern is one of high SST distributed in a horizontally uniform manner. The spring transition includes a midshelf cold tongue. Similar analyses performed on SST anomaly data provide further details of these seasonally varying patterns. It is demonstrated that the GHSOM analysis is more effective in extracting the inherent SST patterns than the widely used EOF method. The underlying patterns in a dataset can be visualized in the SOM array in the same form as the original data, while they can only be expressed in anomaly form in the EOF analysis. Some important features, such as asymmetric SST anomaly patterns of winter/summer and cold/warm tongues, can be revealed by the SOM array but cannot be identified in the lowest mode EOF patterns. Also, unlike the EOF or SOM techniques, the hierarchical structure in the input data can be extracted by the GHSOM analysis.

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“…Santos et al, 2001;Guisande et al, 2001;Borges et al, 2003), or uses pattern recognition techniques to identify favourable environmental periods and compare them with the birth-date distribution of recruits (Hardman-Mountford et al, 2003); second, a Lagrangian approach where particle-tracking models are used to estimate interannual variation in cross-shelf transport and environmental conditions and associate them with corresponding variation in recruitment strength (e.g. Bernsten et al, 1994;Polovina et al, 1999;Griffin et al, Fig.…”

Section: Introductionmentioning

confidence: 99%