Risk Assessment of Mountain Torrents Disaster in Jiangxi Province, China Based on Random Forest Algorithm

2019 ◽  
Author(s):  
Xiuqin FANG ◽  
Xiaojun WU ◽  
Chao ZHOU ◽  
Taoying WU ◽  
Xiaotong DU ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Random Forest ◽  
Jiangxi Province ◽  
Random Forest Algorithm

scholarly journals Financial Credit Risk Control Strategy Based on Weighted Random Forest Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guo Yangyudongnanxin
Keyword(s):  
Risk Assessment ◽  
Random Forest ◽  
Credit Risk ◽  
Control Strategy ◽  
Control Strategies ◽  
Risk Control ◽  
Evaluation Index System ◽  
Risk Level ◽  
Random Forest Algorithm ◽  
Financial Credit

In order to improve the effectiveness of financial credit risk control, a financial credit risk control strategy based on weighted random forest algorithm is proposed. The weighted random forest algorithm is used to classify the financial credit risk data, construct the evaluation index system, and use the analytic hierarchy process to evaluate the financial credit risk level. The targeted risk control strategies are taken according to different risk assessment results. We compared the proposed method with two other methods, and the experimental results show that the proposed method has higher classification accuracy of financial credit data and the risk assessment threshold is basically consistent with the actual results.

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.

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