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Atmospheric Meteorological Effects on Forecasting Daily Lightning Occurrence at Cape Canaveral Space Force Station

Atmospheric Meteorological Effects on Forecasting Daily Ligh...

引用

2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023

作者： Saul, Jon Wagner, Torrey Mbonimpa, Eric Langhals, Brent Graduate School of Engineering and Management Air Force Institute of Technology Data Analytics Certificate Program Wright-Patterson AFBOH United States

ISBN: (纸本)9798350327595

As the Cape Canaveral Space Force Station and Kennedy Space Center increase their launch rate, any process that could assist in the automation of the currently-manual lightning forecast would be valuable. This work examines the possibility of machine-learning assistance with the daily lighting forecast which is produced by the 45th Weather Squadron. A dataset consisting of 34 lightning, pressure, temperature and windspeed measurements taken from 334 daily weather balloon (rawinsonde) launches in the timeframe 2012-2021 was examined. Models were created using recursive feature elimination on logistic regression and XGClassifier algorithms, as well as Bayesian and bandit optimization of neural network (NN) hyperparameters. The modeling process was repeated after eliminating 13 features related to windspeed. The best performing models on both datasets were the optimized NN models, with an F1 metric of 0.79 on the full dataset and 0.66 on the reduced dataset. For comparison, a model that predicted randomly achieved F1 = 0.47 on this dataset. The addition of 13 windspeed-related features more than doubled the complexity of the 21-feature no-wind model while increasing model performance by 13 percentage points. A notable inference from the statistical modeling is that the most important feature from both datasets was the Thompson convective index, which is related to temperature, dewpoint, relative humidity and lapse rate. © 2023 IEEE.

关键词： launch operations lightning forecast lightning indices neural network

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Machine Learning Prediction of DoD Personal Property Shipment Costs

Machine Learning Prediction of DoD Personal Property Shipmen...

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2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023

作者： Tucker, Tiffany Wagner, Torrey Auclair, Paul Langhals, Brent Graduate School of Engineering and Management Air Force Institute of Technology Data Analytics Certificate Program Wright-Patterson AFBOH United States LinQuest Corporation Integrated Analytics & Support BeavercreekOH United States

ISBN: (纸本)9798350327595

U.S. Department of Defense (DoD) personal property moves account for 15% of all domestic and international moves - accurate prediction of their cost could draw attention to outlier shipments and improve budget planning. In this work 136,140 shipments between 13 personal property shipment hubs from April 2022 through March 2023 with a total cost of $1.6B were analyzed. Shipment cost was predicted using recursive feature elimination on linear regression and XGBoost algorithms, as well as through neural network hyperparameter sweeps. Modeling was repeated after removing 28 features related to shipment hub location and branch of service to examine their influence on algorithm performance. The best model resulted from a neural network hyperparameter sweep on the simple 4-feature dataset. That model contained 2 hidden layers of 250 neurons, possessed a mean absolute error (MAE) of $2162 on the holdout dataset, and had an overfitting measure of 0.2%. This model's performance is a substantial improvement over a trivial mean-prediction model that possessed a MAE of $6856. Additionally, the model R2 = 0.87 compared favorably to existing work that achieved R2 = 0.73. Models that included 28 features related to shipment hub location and branch of service did not improve performance, showing that those features are insignificant in predicting shipment cost. All OLS regression and XGBoost models showed that the most influential features (in order) were weight, month of shipment, distance shipped, and days in transit. © 2023 IEEE.

关键词： ensemble methods logistics machine learning neural networks random forest regression

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Predictive Analysis of Defense Language Proficiency Test Outcomes: A Comparative Study Using Neural Networks and Logistic Regression 20th

Predictive Analysis of Defense Language Proficiency Test Out...

引用

20th International Conference on data Science, ICdata 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Smith, Jared Mbonimpa, Eric Wagner, Torrey Langhals, Brent Data Analytics Certificate Program Graduate School of Engineering and Management Air Force Institute of Technology WPAFBOH United States

ISBN: (纸本)9783031858550

Proficiency in foreign languages is crucial for effective global operations, and proficiency can be gained from training courses. In this work, we study the influence of various training courses by analyzing a 9,436-row dataset, including factors such as language, test timing, and their effect on an individual's language test performance. Both logistic regression algorithms and neural networks were employed to analyze the factors influencing language test scores. The key findings reveal that while the neural network model slightly outperformed the logistic regression model in terms of positive class recall (0.69 vs. 0.61), the logistic regression model is preferred due to its greater interpretability and generalizability. Notably, the logistic regression model identified higher initial reading scores and frequent testing as significant predictors of score improvement. Importantly, the logistic regression model achieved an accuracy of 0.78, which significantly surpasses the trivial model's accuracy of 0.51. This study contributes to the field of predictive analytics in language proficiency and provides actionable insights for future research and policy-making in defense language programs. The interpretable logistic regression model can help inform the design of more effective language training courses and testing strategies to enhance global operations. © This is a U.S. government work and not under copyright protection in the U.S.;foreign copyright protection may apply 2025.

关键词： Logistic regression

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Atmospheric Meteorological Effects on Forecasting Daily Lightning Occurrence at Cape Canaveral Space Force Station

Atmospheric Meteorological Effects on Forecasting Daily Ligh...

引用

Computer Science, Computer Engineering, & Applied Computing (CSCE), Congress in

作者： Jon Saul Torrey Wagner Eric Mbonimpa Brent Langhals Data Analytics Certificate Program Graduate School of Engineering and Management Air Force Institute of Technology Wright-Patterson AFB OH

As the Cape Canaveral Space Force Station and Kennedy Space Center increase their launch rate, any process that could assist in the automation of the currently-manual lightning forecast would be valuable. This work examines the possibility of machine-learning assistance with the daily lighting forecast which is produced by the 45th Weather Squadron. A dataset consisting of 34 lightning, pressure, temperature and windspeed measurements taken from 334 daily weather balloon (rawinsonde) launches in the timeframe 2012–2021 was examined. Models were created using recursive feature elimination on logistic regression and XGClassifier algorithms, as well as Bayesian and bandit optimization of neural network (NN) hyperparameters. The modeling process was repeated after eliminating 13 features related to windspeed. The best performing models on both datasets were the optimized NN models, with an F1 metric of 0.79 on the full dataset and 0.66 on the reduced dataset. For comparison, a model that predicted randomly achieved F1 = 0.47 on this dataset. The addition of 13 windspeed-related features more than doubled the complexity of the 21-feature no-wind model while increasing model performance by 13 percentage points. A notable inference from the statistical modeling is that the most important feature from both datasets was the Thompson convective index, which is related to temperature, dewpoint, relative humidity and lapse rate.

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Machine Learning Prediction of DoD Personal Property Shipment Costs

Machine Learning Prediction of DoD Personal Property Shipmen...

引用

Computer Science, Computer Engineering, & Applied Computing (CSCE), Congress in

作者： Tiffany Tucker Torrey Wagner Paul Auclair Brent Langhals Data Analytics Certificate Program Graduate School of Engineering and Management Air Force Institute of Technology Wright-Patterson AFB OH Integrated Analytics & Support LinQuest Corporation Beavercreek OH

U.S. Department of Defense (DoD) personal property moves account for 15% of all domestic and international moves - accurate prediction of their cost could draw attention to outlier shipments and improve budget planning. In this work 136,140 shipments between 13 personal property shipment hubs from April 2022 through March 2023 with a total cost of $1.6B were analyzed. Shipment cost was predicted using recursive feature elimination on linear regression and XGBoost algorithms, as well as through neural network hyperparameter sweeps. Modeling was repeated after removing 28 features related to shipment hub location and branch of service to examine their influence on algorithm performance. The best model resulted from a neural network hyperparameter sweep on the simple 4-feature dataset. That model contained 2 hidden layers of 250 neurons, possessed a mean absolute error (MAE) of $2162 on the holdout dataset, and had an overfitting measure of 0.2%. This model's performance is a substantial improvement over a trivial mean-prediction model that possessed a MAE of $6856. Additionally, the model R 2 = 0.87 compared favorably to existing work that achieved R2 = 0.73. Models that included 28 features related to shipment hub location and branch of service did not improve performance, showing that those features are insignificant in predicting shipment cost. All OLS regression and XGBoost models showed that the most influential features (in order) were weight, month of shipment, distance shipped, and days in transit.

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