Predicting FA-18 Squadron Readiness and Quarterly Flight Hour Execution Using Machine Learning

Abstract

Given manning-training-equipment datasets from Naval FA-18 squadrons, a machine learning model for determining the monthly mean number of mission capable jets per squadron is created. This model is then extended and used as an input to create an ensemble of models determining the flight hour execution of a squadron over a three-month period. The ensemble of models is then used to predict squadron performance and readiness, and can correctly classify a squadron's future performance with 75% accuracy 90-days in advance.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2019
Accession Number
AD1085789

Entities

People

  • Benjamin Michlin
  • Charles Yetman
  • Dean Lee
  • Josh Duclos
  • Rick Cruz
  • Ruey Chang
  • Vincent Siu

Organizations

  • Naval Information Warfare Center Pacific

Tags

Communities of Interest

  • Autonomy
  • C4I

DTIC Thesaurus Topics

  • Air Force
  • Command And Control
  • Department Of Defense
  • Governments
  • Information Operations
  • Information Warfare
  • Learning
  • Machine Learning
  • National Governments
  • Naval Warfare
  • Squadrons
  • Technical Information Centers
  • Training
  • United States
  • United States Government
  • Warfare

Readers

  • Aviation Science / Aeronautics.
  • Logistics and Supply Chain Management.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks