Service-Oriented Atmospheric Radiances (SOAR): Gridding and Analysis Services for Multisensor Aqua IR Radiance Data for Climate Studies

Halem, M.; Most, N.; Tilmes, Curt; Stewart, K. H.; Yesha, Yaacov; Chapman, David; Nguyen, Phuong

doi:10.1109/tgrs.2008.2001854

Cited by 8 publications

(2 citation statements)

References 6 publications

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“…We have shown that combining multioutput sensor data with advanced AI techniques offers a powerful avenue, especially to model nonlinear processes such as air quality, as was done in this study. Thanks to the collection of new and larger datasets, future work should focus on developing new techniques that can analyze the problem as time series to further improve prediction performance, possibly as done in [64,65,66]. Finally, interested readers are recommended to consider cross-interference, sensitivity, and response time of sensors [67] in AI models developed to predict air quality.…”

Section: Discussionmentioning

confidence: 99%

Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data

Phi

et al. 2019

Sensors

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Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO). In this work, the concentrations of NO2 and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO2 and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO2 and CO.

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Section: Discussionmentioning

confidence: 99%

Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data

Phi

et al. 2019

Sensors

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“…We describe the problem and relevance of gridding satellite data [7] and present the current non-Hadoop method for gridding these data. We also describe, for comparison, a proposed Hadoop MapReduce approach [8].…”

Section: Introductionmentioning

confidence: 99%

Cloud Computing for Satellite Data Processing on High End Compute Clusters

Golpayegani

Halem

2009

2009 IEEE International Conference on Cloud Computing

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Hadoop is a Distributed Filesystem and MapReduce framework originally developed for search applications byGoogle and subsequently adopted by the Apache foundation as an open source system. We propose that this parallel computing framework is well suited for a variety of service oriented science applications and, in particular, for satellite data processing of remote sensing systems. We show that, by installing Hadoop on a cluster of IBM PowerPC blade clusters, we can efficiently process multiyear remote sensing data, expect to see speed performance improvements over conventional multi-processor methodologies, and have more memory efficient implementation allowing for finer grid resolutions. Moreover, these improvements can be met without significant changes in coding structure.

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