Predicting the Category of Fire Department Operations. Pirklbauer, K. & Findling, R. D. In Emerging Research Projects and Show Cases Symposium (SHOW 2019), 2019.
Predicting the Category of Fire Department Operations [pdf]Paper  abstract   bibtex   
Voluntary fire departments have limited human and material resources. Machine learning aided prediction of fire department operation details can benefit their resource planning and distribution. While there is previous work on predicting certain aspects of operations within a given operation category, operation categories themselves have not been predicted yet. In this paper we propose an approach to fire department operation category prediction based on location, time, and weather information, and compare the performance of multiple machine learning models with cross validation. To evaluate our approach, we use two years of fire department data from Upper Austria, featuring 16.827 individual operations, and predict its major three operation categories. Preliminary results indicate a prediction accuracy of 61%. While this performance is already noticeably better than uninformed prediction (34% accuracy), we intend to further reduce the prediction error utilizingmore sophisticated features and models.
@InProceedings{Pirklbauer_19_PredictingCategoryFire,
  author    = {Kevin Pirklbauer and Rainhard Dieter Findling},
  booktitle = {Emerging Research Projects and Show Cases Symposium ({SHOW} 2019)},
  title     = {Predicting the Category of Fire Department Operations},
  year      = {2019},
  abstract  = {Voluntary fire departments have limited human and material resources. Machine learning aided prediction of fire department operation details can benefit their resource planning and distribution. While there is previous work on predicting certain aspects of operations within a given operation category, operation categories themselves have not been predicted yet. In this paper we propose an approach to fire department operation category prediction based on location, time, and weather information, and compare the performance of multiple machine learning models with cross validation. To evaluate our approach, we use two years of fire department data from Upper Austria, featuring 16.827 individual operations, and predict its major three operation categories. Preliminary results indicate a prediction accuracy of 61\%. While this performance is already noticeably better than uninformed prediction (34% accuracy), we intend to further reduce the prediction error utilizingmore sophisticated features and models.},
  url_Paper = {http://ambientintelligence.aalto.fi/paper/momm2019_fire_department_operation_prediction.pdf},
group = {ambience}
}
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