Mining aviation data to understand impacts of severe weather on airspace system performance

Nazeri, Zohreh; Zhang, Jianping

doi:10.1109/itcc.2002.1000441

Cited by 17 publications

(12 citation statements)

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“…Risk and safety have always been important considerations in aviation. With the rapid growth in air travel, flight delays, cancellations and incidents/accidents have also dramatically increased in recent years (23) . Aviation accidents may result in human injury or even death.…”

Section: Related Workmentioning

confidence: 99%

Prediction of warning level in aircraft accidents using data mining techniques

Christopher¹,

Balamurugan²

2014

Aeronaut. j. (1968)

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Data mining is a data analysis process which is designed for large amounts of data. It proposes a methodology for evaluating risk and safety and describes the main issues of aircraft accidents. We have a huge amount of knowledge and data collection in aviation companies. This paper focuses on different feature selectwindion techniques applied to the datasets of airline databases to understand and clean the dataset. CFS subset evaluator, consistency subset evaluator, gain ratio feature evaluator, information gain attribute evaluator, OneR attribute evaluator, principal components attribute transformer, ReliefF attribute evaluatoboundar and symmetrical uncertainty attribute evaluator are used in this study in order to reduce the number of initial attributes. The classification algorithms, such as DT, KNN, SVM, NN and NB, are used to predict the warning level of the component as the class attribute. We have explored the use of different classification techniques on aviation components data. For this purpose Weka software tools are used. This study also proves that the principal components attribute with decision tree classifier would perform better than other attributes and techniques on airline data. Accuracy is also very highly improved. This work may be useful for an aviation company to make better predictions. Some safety recommendations are also addressed to airline companies.

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Section: Related Workmentioning

confidence: 99%

Prediction of warning level in aircraft accidents using data mining techniques

Christopher¹,

Balamurugan²

2014

Aeronaut. j. (1968)

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“…Airline companies may use data mining in order to fuel cost optimization, planning take into consideration weather conditions, passenger analysis, cargo optimization, airport situation revenue per flight, profit per flight, cost per seat or more detailed one catering and handling expenses per seat. Nazeri and Zhang [8] applied a data mining approach for analysis of whether impact on NAS performance. They applied decision tree learning algorithms C5.0 and K mean clustering algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Missing values are dealt with by splitting the corresponding instances into pieces (i.e. as in C4.5) [8].…”

Section: Classifierstreesreptreementioning

confidence: 99%

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2011

IJDKP

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Flight delay has been the fiendish problem to the world's aviation industry, so there is very important significance to research for computer system predicting flight delay propagation. Extraction of hidden information from large datasets of raw data could be one of the ways for building predictive model. This paper describes the application of classification techniques for analysing the Flight delay pattern in Egypt Airline's Flight dataset. In this work, four decision tree classifiers were evaluated and results show that the REPTree have the best accuracy 80.3% with respect to Forest, Stump and J48. However, four rules based classifiers were compared and results show that PART provides best accuracy among studied rule-based classifiers with accuracy of 83.1%. By analysing running time for all classifiers, the current work concluded that REPtree is the most efficient classifier with respect to accuracy and running time. Also, the current work is extended to apply of Apriori association technique to extract some important information about flight delay. Association rules are presented and association technique is evaluated.

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“…In fact, there are few approaches known by the authors that use data mining techniques in the aviation domain. Earlier aviation studies (Nazeri, 2002;Callahan, 2001) use static, specialized techniques for aviation security and studies in weather. These earlier approaches do not leverage current data modeling approaches or follow a general purpose design.…”

Section: Related Workmentioning

confidence: 99%

Experience Report

Blake

Singh

Williams

et al. 2006

International Journal of Information Technology and Web Engineering

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Organizations are beginning to apply data mining and knowledge discovery techniques to their corporate data sets, thereby enabling the identification of trends and the discovery of inductive knowledge. Many times, traditional transactional databases are not optimized for analytical processing and must be transformed. This article proposes the use of modular components to decrease the overall amount of human processing and intervention necessary for the transformation process. Our approach configures components to extract data-sets using a set of "extraction hints". Our framework incorporates decentralized, generic components that are reusable across domains and databases. Finally, we detail an implementation of our component-based framework for an aviation data set.

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Mining aviation data to understand impacts of severe weather on airspace system performance

Cited by 17 publications

References 1 publication

Prediction of warning level in aircraft accidents using data mining techniques

Prediction of warning level in aircraft accidents using data mining techniques

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