Using self-organizing maps to determine observation threshold limit predictions in highly variant data

Christopher A. Paganoni; K. C. Chang; Michael B. Robblee

doi:10.1117/12.667802

17 May 2006 Using self-organizing maps to determine observation threshold limit predictions in highly variant data

Christopher A. Paganoni, K. C. Chang, Michael B. Robblee

Proceedings Volume 6235, Signal Processing, Sensor Fusion, and Target Recognition XV; 62351H (2006) https://doi.org/10.1117/12.667802
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

A significant data quality challenge for highly variant systems surrounds the limited ability to quantify operationally reasonable limits on the data elements being collected and provide reasonable threshold predictions. In many instances, the number of influences that drive a resulting value or operational range is too large to enable physical sampling for each influencer, or is too complicated to accurately model in an explicit simulation. An alternative method to determine reasonable observation thresholds is to employ an automation algorithm that would emulate a human analyst visually inspecting data for limits. Using the visualization technique of self-organizing maps (SOM) on data having poorly understood relationships, a methodology for determining threshold limits was developed. To illustrate this approach, analysis of environmental influences that drive the abundance of a target indicator species (the pink shrimp, Farfantepenaeus duorarum) provided a real example of applicability. The relationship between salinity and temperature and abundance of F. duorarum is well documented, but the effect of changes in water quality upstream on pink shrimp abundance is not well understood. The highly variant nature surrounding catch of a specific number of organisms in the wild, and the data available from up-stream hydrology measures for salinity and temperature, made this an ideal candidate for the approach to provide a determination about the influence of changes in hydrology on populations of organisms.

Citation Download Citation

Christopher A. Paganoni, K. C. Chang, and Michael B. Robblee "Using self-organizing maps to determine observation threshold limit predictions in highly variant data", Proc. SPIE 6235, Signal Processing, Sensor Fusion, and Target Recognition XV, 62351H (17 May 2006); https://doi.org/10.1117/12.667802

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KEYWORDS

Data modeling

Hydrology

Neurons

Temperature metrology

Systems modeling

Brain mapping

Data centers

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