An evolutionary system for recognition and tracking of synoptic-scale storm systems

Parikh, Jo Ann; DaPonte, John S.; Vitale, Joseph N.; Tselioudis, G.

doi:10.1016/s0167-8655(99)00110-5

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…For example, the prediction of the maximum power point of photovoltaic systems is tackled in [38] as a clustering problem, solved by a genetic K-means algorithm. The classification and track of storms or weather systems can also be stated as a clustering approach [32]. Moreover, the climatological analysis about relations between winds, precipitation or even pollution and pressure patterns is a classical problem that can be solved as a clustering problem [7e9,33e35].…”

Section: A Brief Review Of Clustering Algorithmsmentioning

confidence: 99%

Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing

Carro-Calvo¹,

Salcedo‐Sanz²,

Kirchner-Bossi³

et al. 2011

Energy

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Section: A Brief Review Of Clustering Algorithmsmentioning

confidence: 99%

Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing

Carro-Calvo¹,

Salcedo‐Sanz²,

Kirchner-Bossi³

et al. 2011

Energy

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Section: Introductionmentioning

confidence: 99%

“…Also genetic algorithms, self-organized mapping systems and techniques of nearest neighbor were used by Henke, Sieglaff and Parikh in [3]- [5]. Furthermore, tracking techniques have been implemented using satellite image processing and neural networks (NN) in Chandan [5]. However, the considerable number of factors to be considered leads to the necessity of performing the analysis by areas, because clouds are one of the main sources of uncertainty in the development of models for climate prediction and its conformation depends on geographic conditions.…”

Section: Introductionmentioning

confidence: 99%

Técnicas inteligentes para la identificación y el seguimiento de fenómenos meteorológicos que podrían afectar la seguridad de vuelo

Zuluaga

Vargas²,

Reina³

2017

Ciencia y Poder Aéreo

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Resumo: Na aviação, os fenômenos meteorológicos são um dos aspectos mais importantes a ter em conta em todas as etapas do vôo, do planejamento ao pouso. O desenvolvimento de sistemas de previsão do tempo aplicados à aviação pode apoiar o processo de tomada de decisão de controladores e pilotos de tráfego aéreo, facilitando a análise de variáveis meteorológicas e fornecendo uma primeira interpretação disponível para todos os usuários do sistema de ar . Por esta razão, o Centro de Desenvolvimento Tecnológico para a Defesa (CETAD) tem como objetivo principal neste documento descrever os resultados do desenvolvimento de uma metodologia sistematizada que utiliza técnicas inteligentes para a detecção, identificação e monitoramento de qualquer tipo de treinamento que, devido às suas características pode representar um risco para a aviação, gerando, por sua vez, informações de suporte para o controlador de tráfego aéreo. Para isso, é necessário primeiro identificar as formações convectivas, classificá-las, filtrar o ruído e individualizá-las. Este tipo de processo pode ser automatizado através da análise inteligente Resumen: en la aviación, los fenómenos meteorológicos son uno de los aspectos más importantes para tener en cuenta en todas las etapas de vuelo, desde la planificación hasta el aterrizaje. El desarrollo de sistemas de predicción meteorológica aplicados a la aviación puede apoyar el proceso de toma de decisiones de los controladores de tráfico aéreo y los pilotos, facilitando el análisis de las variables meteorológicas y proporcionando una primera interpretación a disposición de todos los usuarios del sistema aéreo. Por esta razón el Centro de desarrollo Tecnológico para la Defensa (CETAD) tiene como principal objetivo en este documento describir los resultados del desarrollo de una metodología sistematizada que utiliza técnicas inteligentes para la detección, identificación y seguimiento de cualquier tipo de formación que por sus características pueda representar un riesgo para la aviación, generando a su vez información de soporte al controlador aéreo. Para esto es necesario primero identificar las formaciones convectivas, clasificarlas, filtrar el ruido e individualizarlas. Este tipo de procesos pueden ser automatizados a través del análisis inteligente de productos disponibles en cualquier sistema aéreo como las imágenes satelitales multiespectrales. Posterior a una identificación, se deben determinar un grupo de características que permitan desarrollar algoritmos eficientes capaces de realizar un seguimiento del comportamiento de la formación convectiva, que permita generar pronósticos de las características de los sistemas convectivos en el corto plazo, para lo que se requiere conocer otras variables como el viento en las áreas de análisis. Este tipo de aplicaciones integradas a los sistemas de control de tráfico aérea disminuirían los riesgos debidos factores meteorológicos.Palabras clave: sistemas de control de aire, análisis meteorológico, inteligencia artificial, imá-genes satelitales, sistemas de p...

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“…' A genetic algorithm approach based on template matching and discriminant strength was used by Carbonaro and Zingarretti5 to track video recorded image sequences. Parikh et al 6 have outlined a methodology for using evolutionary techniques for recognition and tracking of storm cloud systems and compared results to statistical techniques.7'8…”

Section: Introductionmentioning

confidence: 99%

<title>Comparison of a geometric-based and an evolutionary technique for tracking storm systems</title>

et al. 2000

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The objective of this study is to compare geometric-based and evolutionary techniques for tracking storm systems from sequences of satellite images. Analysis was applied to the International Satellite Cloud Climatology Project (ISCCP) low resolution Dl database for selected storm systems during the month of September, 1988. During this time period there were two exceptionally long tracks of major hurricane systems, Hurricanes Gilbert and Helene. Cloud top pressure and and cloud optical thickness were used to identify storm systems. The ability of the geometricbased and evolutionary techniques to generate tracks through storm regions was assessed. Differences in final tracking results between the two techniques resulted not only from the differences in methodology but also from differences in the type of preprocessed input used by each of the techniques. Tracking results were compared to results disseminated by the Colorado State/Tropical Prediction Center and maintained by by the National Hurricane Center in Miami, Florida. For the hurricanes investigated in this study, both techniques were able to generate tracks which followed either most or some of the portions of the hurricanes. The evolutionary algorithm was in general able to maintain good continuity along the tracks but, with no knowledge of overall region movement, was unable to discern which of two possible directions would be best to pursue in cases where there were two or more equally close storm system components. The geometric method was able to maintain a smooth track close to the course of the hurricane except for confusion primarily at the beginning and/or end of tracks.

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An evolutionary system for recognition and tracking of synoptic-scale storm systems

Cited by 10 publications

References 16 publications

Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing

Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing

Técnicas inteligentes para la identificación y el seguimiento de fenómenos meteorológicos que podrían afectar la seguridad de vuelo

<title>Comparison of a geometric-based and an evolutionary technique for tracking storm systems</title>

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