Formal verification of a machine learning tool for runway configuration assistance
Clicks: 1
ID: 309994
2025
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #395 of 490 articles by views in Frontiers in surgery
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 490 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
This study explores the use of formal verification techniques to evaluate the efficacy of suggestions made by the Runway Configuration Assistance (RCA) tool, a machine learning-based decision support system that our group developed independently. By using model-checking approaches, in particular Computation Tree Logic (CTL), this study verifies the compliance of the RCA tool with predefined safety regulations under different conditions of surface winds. By simulating a range of scenarios at three major US airports, Charlotte Douglas International Airport (CLT), Denver International Airport (DEN), and Dallas-Fort Worth International Airport (DFW), we thoroughly test the predictions of the tool to ensure that they meet strict safety margins with respect to crosswind and tailwind. The application of formal verification methods provides a strict analysis of the RCA tool, enhancing its validity and utility for possible implementation in an operational environment. Initially, a Monte Carlo simulation is carried out to analyze all possible wind conditions both velocity-wise and direction-wise. This part is intended to rigorously test the model against extreme, worst-case conditions to evaluate its performance. Second, we improve our methodology by performing simulations driven by realistic scenarios informed by actual historical data. This approach allows for a more accurate reflection of typical wind conditions (seen in the test airport) and provides a robust assessment of the model’s effectiveness in maintaining safety standards under realistic environmental conditions. The model-checking reveals that overall 70% and 94% of the predictions satisfy the safety criteria in worst-case and realistic wind scenarios, respectively.
| Reference Key |
imported_1768911206_696f71662d23f
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Kalyanam, Krishna |
| Journal | Frontiers in surgery |
| Year | 2025 |
| DOI |
10.3389/fpace.2025.1463425
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.