A machine learning-based predictive model for risk assessment in airport areas

被引：0

作者：

Gugliandolo, Giovanni ^{[1
]}

Caccamo, Maria Teresa ^{[1
]}

Castorina, Giuseppe ^{[1
]}

Chillemi, Domenica Letizia ^{[1
]}

Famoso, Fabio ^{[1
]}

Munao, Gianmarco ^{[1
]}

Raffaele, Marcello ^{[1
]}

Schifilliti, Valeria ^{[1
]}

Semprebello, Agostino ^{[1
]}

Magazu, Salvatore ^{[1
]}

机构：

[1] Univ Messina, MIFT Dept, I-98166 Messina, Italy

来源：

2021 IEEE 8TH INTERNATIONAL WORKSHOP ON METROLOGY FOR AEROSPACE (IEEE METROAEROSPACE) | 2021年

关键词：

aerospace safety; machine learning; volcanic ash;

D O I：

10.1109/METROAEROSPACE51421.2021.9511695

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

Among the issues affecting the aeronautical field, it is worth highlighting the impact that volcanic eruptions have on airport infrastructures. Such events can lead to delays and flights cancellations. In addition, airports may need to be shut down in order to restore the runway conditions with an important financial impact on the airport and airline companies as well as inconveniences for travelers. Moreover, volcanic ashes suspended in air represent a significant hazard for aircraft in flight: they limit the visibility and can seriously affect both mechanical parts and electronic components. The scope of this work is to develop a machine learning-based model able to predict such events in order to optimize the airport management in case of such extreme and uncontrollable phenomenon.

引用

页码：53 / 57

页数：5

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