Improved Random Forest Algorithm in the Training of Civil Aviation Transportation Professionals with Higher Vocational Colleges

2022 ◽  
pp. 1657-1661
Author(s):  
Xiaoshuo Zhao
Keyword(s):  
Random Forest ◽  
Civil Aviation ◽  
Random Forest Algorithm ◽  
Aviation Transportation ◽  
Vocational Colleges

scholarly journals A Risk Prediction Model of Hard Landing Based on Random Forest Algorithm

2021 ◽  
Vol 257 ◽  
pp. 02080
Author(s):  
Ruishan Sun ◽  
Chongfeng Li
Keyword(s):  
Random Forest ◽  
Random Forests ◽  
Safety Management ◽  
Influence Factors ◽  
Recall Rate ◽  
Civil Aviation ◽  
Analysis Method ◽  
Random Forest Algorithm ◽  
Flight Data ◽  
Quantitative Analysis Method

Landing safety is a hot issue in civil aviation safety management. In order to fully mine the influence factors of hard landing in flight data and effectively predict the risk of hard landing, the random forest algorithm was introduced. Firstly, this paper qualitatively analyzed the influence factors of hard landing, and chose the features of the model based on the flight data. Secondly, this paper gives a quantitative analysis method of the importance of features based on Gini index. Finally, for the dataset of hard landing was class-imbalanced, the model was training based on SMOTE method. Then, the random forests classifier was built and verified by real flight data. The results showed that the recall rate of the model was 85.50%. The model can not only effectively prevent the occurrence of hard landing, but also provide a method reference for airlines to apply data mining to improve the ability of flight events management.

METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

2019 ◽  
pp. 71-76
Author(s):  
A.E. Semenov
Keyword(s):  
Random Forest ◽  
Develop Model ◽  
Random Forest Algorithm ◽  
Saint Petersburg ◽  
Pedestrian Navigation ◽  
Factors Influencing ◽  
The City

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

Classification and photometric redshift estimation of quasars in photometric surveys

2020 ◽  
Vol 15 (S359) ◽  
pp. 40-41
Author(s):  
L. M. Izuti Nakazono ◽  
C. Mendes de Oliveira ◽  
N. S. T. Hirata ◽  
S. Jeram ◽  
A. Gonzalez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbour ◽  
Random Forest Algorithm ◽  
Photometric Redshift ◽  
Using Data

AbstractWe present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.

Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices

2021 ◽  
Vol 252 ◽  
pp. 106906
Author(s):  
Guomin Shao ◽  
Wenting Han ◽  
Huihui Zhang ◽  
Shouyang Liu ◽  
Yi Wang ◽  
...  
Keyword(s):  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Indices ◽  
Crop Coefficient ◽  
Random Forest Algorithm ◽  
Maize Crop ◽  
Area Index
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