The usefulness of NLP techniques for predicting peaks in firefighter interventions due to rare events

• Published in: Neural computing & applications Vol. 34; no. 12; pp. 10117 - 10132
• Main Authors: Cerna, Selene, Guyeux, Christophe, Laiymani, David
• Format: Journal Article
• Language: English
• Published: London Springer London 01.06.2022; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; COVID-19; Data Mining and Knowledge Discovery; Feature extraction; Fire departments; Firefighters; Floods; Forest & brush fires; Heating; Image Processing and Computer Vision; Intervention; Meteorological data; Natural language processing; Original; Original Article; Probability and Statistics in Computer Science; Rain; Rescue operations; Time series; Workloads; Regression; Natural language processing; BERT; Firemen activity prediction
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-06996-x

In some countries such as France, the number of operations assisted by firefighters has shown an almost linear increase over the years, contrary to their resource capacity. For this reason, predicting the number of interventions has become a necessity. Initially, time series models were developed with several types of qualitative and quantitative features, including the alert level of the bulletins, to predict the operational load. We realized that interventions related to human activities are quite predictable. However, the recognition of interventions due to rare events such as storms or floods needs more than quantitative meteorological data to be identified, since there are almost always zero cases. Thus, this work proposes the application of natural language processing techniques, namely long short-term memory, convolutional neural networks, FlauBERT, and CamemBERT to extract features from the texts of weather bulletins in order to recognize periods with peak interventions, where the intense workload of firefighters is caused by rare events. Four categories identified as Emergency Person Rescue, Total Person Rescue, interventions related to Heating, and Storm/Flood were our targets for the multilabel classification models developed. The results showed a remarkable accuracy of 80%, 86%, 92%, and 86% for Emergency Rescue People, Total Rescue People, Heating, and Storm/Flood, respectively.