Data mining application on aviation accident data for predicting topmost causes for accidents

Koteeswaran, S.; Malarvizhi, N.; Kannan, E.; Sasikala, S.; Geetha, S.

doi:10.1007/s10586-017-1394-2

Cited by 16 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several works have tackled the safety topic in aviation through DM. Some of the most popular are: ( Pagels, 2015 ) where 4 methods are applied for the identification of safety issues before they happen; ( Koteeswaran et al, 2019 ) which develops a method for predicting accidents and ( Merzbacher, 2019 ) which focuses on detecting explosives.…”

Section: Literature Reviewmentioning

confidence: 99%

Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance

Pérez-Campuzano

Andrada

Ortega

et al. 2022

Journal of Air Transport Management

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance

Pérez-Campuzano

Andrada

Ortega

et al. 2022

Journal of Air Transport Management

View full text Add to dashboard Cite

“…Priyam Mathur et al proposed a logistic regression model to predict the possible aviation accidents (Mathur et al, 2017). S. Koteeswaran et al combined k-nearest nearest neighbor classifier and relevancebased feature selection algorithm for mining aviation accident data (Koteeswaran et al, 2019). Xiaomei Ni et al employed deep belief networks and principal component analysis to predict the rate of serious flight accidents (Ni et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…S. Koteeswaran et al . combined k-nearest nearest neighbor classifier and relevance-based feature selection algorithm for mining aviation accident data (Koteeswaran et al ., 2019). Xiaomei Ni et al .…”

Section: Introductionmentioning

confidence: 99%

Improved gray correlation analysis and combined prediction model for aviation accidents

Sun

Peng

et al. 2023

View full text Add to dashboard Cite

PurposeThe purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.Design/methodology/approachThis paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors.FindingsThe global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models.Originality/valueUnlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management.

show abstract

“…Koteeswaran et al [5] proposed an aviation accident prediction method that combines k-Nearest Neighbor (k-NN) and correlation-based feature selection method. This new method can detect risks by predicting the causes of accidents and improve the aviation management system.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Approach to Modeling Aviation Safety Incidents

Shi¹,

Cao²,

Zurada³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

Due to the complexity of aviation safety operations, the number of flight incidents continues to rise. The Aviation Safety Reporting System (ASRS) contains the largest collection of such incidents. Efficient and effective analysis of these incidents remains a challenge. This paper proposes a new approach to analyze aviation safety records using deep learning methods to improve incident classification. The proposed approach, CNN-LSTM, combines the characteristics of convolutional neural network (CNN) and long short-term memory (LSTM) neural network, and a distributed computing method to model aviation safety data. The five machine learning methods Logistic Regression, Naive Bayes, Random Forest, Support Vector Machine, Multi-layer Perceptron were used to compare with CNN-LSTM. The results show that CNN-LSTM model can significantly improve the accuracy rates of classification for aviation safety incident reports using Word2Vec. The distributed platform in Spark with clusters can make full use of computing resources when processing textual data from ASRS, reducing time-consumption greatly when compared with machine learning algorithms running on a standalone computer. Timely and accurate identification of causes of reported incidents is important. The results of this study demonstrate a new approach to improve both accuracy and efficiency in incident cause identification.

show abstract

Data mining application on aviation accident data for predicting topmost causes for accidents

Cited by 16 publications

References 13 publications

Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance

Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance

Improved gray correlation analysis and combined prediction model for aviation accidents

An Innovative Approach to Modeling Aviation Safety Incidents

Contact Info

Product

Resources

About