Alise Danielle Midtfjord

• Midtfjord, Alise Danielle; De Bin, Riccardo & Huseby, Arne Bang (2022). A boosting model for survival analysis with dependent censoring. In Torelli, Nicola; BELLIO, RUGGERO & MUGGEO, VITO (Ed.), Proceedings of the 36th International Workshop on Statistical Modelling. EUT Edizioni Università di Trieste. ISSN 978-88-5511-309-0.
• Midtfjord, Alise Danielle; De Bin, Riccardo & Huseby, Arne Bang (2022). A decision support system for safer airplane landings: Predicting runway conditions using XGBoost and explainable AI. Cold Regions Science and Technology. ISSN 0165-232X. 199. doi: 10.1016/j.coldregions.2022.103556. Full text in Research Archive
• Midtfjord, Alise Danielle & Huseby, Arne Bang (2021). A Machine Learning Approach to Assess Runway Conditions Using Weather Data. In Castanier, Bruno; Cepin, Marko; Bigaud, David & Berenguer, Christophe (Ed.), Proceedings of the 31st European Safety and Reliability Conference. Research Publishing Services. ISSN 978-981-18-2016-8. p. 833–840. doi: 10.3850/978-981-18-2016-8_474-cd.
• Midtfjord, Alise Danielle; De Bin, Riccardo & Huseby, Arne Bang (2021). A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data. arXiv.org. ISSN 2331-8422. Show summary

  The presence of snow and ice on runway surfaces reduces the available tire-pavement friction needed for retardation and directional control and causes potential economic and safety threats for the aviation industry. To activate appropriate safety procedures, pilots need accurate and timely information on the actual runway surface conditions. In this study, XGBoost is used to create a combined runway assessment system, which includes a classification model to identify slippery conditions and a regression model to predict the level of slipperiness. The models are trained on weather data and runway reports. The runway surface conditions are represented by the tire-pavement friction coefficient, which is estimated from flight sensor data from landing aircrafts. The XGBoost models are combined with SHAP approximations to provide a reliable decision support system for airport operators and pilots, which can contribute to safer and more economic operations of airport runways. To evaluate the performance of the prediction models, they are compared to several state-of-the-art runway assessment methods. The XGBoost models identify slippery runway conditions with a ROC AUC of 0.95, predict the friction coefficient with a MAE of 0.0254, and outperforms all the previous methods. The results show the strong abilities of machine learning methods to model complex, physical phenomena with a good accuracy.

• Midtfjord, Alise Danielle & Huseby, Arne (2020). Estimating Runway Friction Using Flight Data. In Baraldi, Piero; Di Maio, Francesco P. & Zio, Enrico (Ed.), e-proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL2020 PSAM15). Research Publishing Services. ISSN 9789811485930. p. 9–16.

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• Midtfjord, Alise Danielle (2021). A Machine Learning Approach to Assess Runway Conditions Using Weather Data.
• Midtfjord, Alise Danielle (2021). A Machine Learning Approach to Safer Airplane Landings.
• Midtfjord, Alise Danielle (2021). A decision support system for safer airplane landings: predicting runway conditions using XGBoost and explainable AI.
• Midtfjord, Alise Danielle (2021). The Fundamentals of AI.
• Midtfjord, Alise Danielle (2020). Development of a data-driven warning system for runway conditions.
• Midtfjord, Alise Danielle (2020). Estimating Runway Friction Using Flight Data.
• Midtfjord, Alise Danielle (2020). Estimating Runway Friction Using Flight Data. Show summary

  During the winter season, contamination of runway surfaces with snow, ice, or slush causes potential economic and safety threats for the aviation industry. The presence of these materials reduces the available tire-pavement friction needed for retardation and directional control. Therefore, pilots operating on contaminated runways need accurate and timely information on the actual runway surface conditions. Avinor, the company that operates most civil airports in Norway, have developed an integrated runway information system, called IRIS, currently used on 16 Norwegian airports. The system uses a scenario approach to identify slippery conditions. In order to validate the scenario model, it is necessary to estimate runway friction. The present paper outlines how this can be done using flight data from the Quick Access Recorder (QAR) of Boeing 737-600/700/800 NG airplanes. Data such as longitudinal acceleration, airspeed, ground speed, flap settings, engine speed, brake pressures are sampled at least each second during landings. The paper discusses some of the challenges with this. In particular, issues related to calibration of data are considered, and two different regression methods are compared.

• Midtfjord, Alise Danielle (2020). Explainable Artificial Intelligence (XAI).
• Midtfjord, Alise Danielle (2020). Explainable Artificial Intelligence: How to make AI responsible. Show summary

  As the use of “black box” algorithms such as deep learning increases rapidly, several challenges related to ethics and law arise. These systems are not capable of explaining their decisions and actions to human users, making it difficult to make sure they follow the laws are morals of our society. Explainable Artificial Intelligence (XAI) addresses these issues by creating understandable explanations of how and why AI systems arrive at their decisions. In this presentation you will learn about some of the moral challenges rising with the use of artificial intelligence, what explainable artificial intelligence is and how it can address a lot of these challenges and contribute to creating trustworthy and responsible AI.

• Grøndahl, Aurora Rosvoll; Midtfjord, Alise Danielle; Langberg, Geir Severin Rakh Elvatun; Tomic, Oliver; Indahl, Ulf Geir & Knudtsen, Ingerid Skjei [Show all 9 contributors for this article] (2019). Prediction of treatment outcome for head and neck cancers using radiomics of PET/CT images. Radiotherapy and Oncology. ISSN 0167-8140. 133, p. 526–526.
• Langberg, Geir Severin; Grøndahl, Aurora Rosvoll; Midtfjord, Alise Danielle; Tomic, Oliver; Liland, Kristian Hovde & Knudtsen, Ingerid Skjei [Show all 9 contributors for this article] (2019). Establishing a complete radiomics framework for biomarker identification and outcome prediction using PET/CT images of head & neck cancers.
• Midtfjord, Alise Danielle & Futsæther, Cecilia Marie (2018). Prediksjon av behandlingsutfall for hode-og halskreft ved bruk av radiomics av PET/CT-bilder. Norwegian University of Life Sciences, Ås.

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