Abstract
In several areas of the world such as France, fire brigades are facing a constant increase in the number of their commitments, some of the main reasons are related to the growth and aging of the population and others to global warming. This increase occurs principally in constant human and material resources, due to the financial crisis and the disengagement of the states. Therefore, forecasting the number of future interventions will have a great impact on optimizing the number and the type of on-call firefighters, making it possible to avoid too few firefighters available during peak load or an oversized guard during off-peak periods. These predictions are viable, given firefighters’ labor is conditioned by human activity in general, itself correlated to meteorological data, calendars, etc. This article aims to show that machine learning tools are mature enough at present to allow useful predictions considering rare events such as natural disasters. The tools chosen are XGBoost and LSTM, two of the best currently available approaches, in which the basic experts are decision trees and neurons. Thereby, it seemed appropriate to compare them to determine if they can forecast the firefighters’ response load and if so, if the results obtained are comparable. The entire process is detailed, from data collection to the predictions. The results obtained prove that such a quality prediction is entirely feasible and could still be improved by other techniques such as hyperparameter optimization.
This work was supported by the EIPHI Graduate School (contract “ANR-17-EURE-0002”), by the Region of Bourgogne Franche-Comté CADRAN Project, by the Interreg RESponSE project, and by the SDIS25 firemen brigade. We also thank the supercomputer facilities of the Mésocentre de calcul de Franche-Comté.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ministère de l’ecologie, du développement durable et de l’energie. http://www.hydro.eaufrance.fr/
Alajali, W., Zhou, W., Wen, S.: Traffic flow prediction for road intersection safety. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, October 2018. https://doi.org/10.1109/smartworld.2018.00151
Cerna, S., Guyeux, C., Arcolezi, H.H., Lotufo, A.D.P., Couturier, R., Royer, G.: Long short-term memory for predicting firemen interventions. In: 6th International Conference on Control, Decision and Information Technologies (CoDIT 2019), Paris, France, April 2019. https://doi.org/10.1109/codit.2019.8820671
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2016, pp. 785–794. ACM, New York (2016). https://doi.org/10.1145/2939672.2939785
Chollet, F., et al.: Keras (2015). https://keras.io
Du, S., Li, T., Gong, X., Yang, Y., Horng, S.J.: Traffic flow forecasting based on hybrid deep learning framework. In: 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, November 2017. https://doi.org/10.1109/iske.2017.8258813
Géron, A.: Hands-On Machine Learning with Scikit-Learn and TensorFlow, vol. 1, 1st edn. O’Reilly Media, Sebastopol (2017)
Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Springer, Berlin/Heidelberg (2012). https://doi.org/10.1007/978-3-642-24797-2
Greff, K., Srivastava, R.K., Koutnik, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2017). https://doi.org/10.1109/tnnls.2016.2582924
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Liu, Y., Wang, Y., Yang, X., Zhang, L.: Short-term travel time prediction by deep learning: a comparison of different LSTM-DNN models. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, October 2017. https://doi.org/10.1109/itsc.2017.8317886
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Shi, X., Li, Q., Qi, Y., Huang, T., Li, J.: An accident prediction approach based on XGBoost. In: 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, November 2017. https://doi.org/10.1109/iske.2017.8258806
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Cerna, S., Guyeux, C., Arcolezi, H.H., Couturier, R., Royer, G. (2020). A Comparison of LSTM and XGBoost for Predicting Firemen Interventions. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1160. Springer, Cham. https://doi.org/10.1007/978-3-030-45691-7_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-45691-7_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45690-0
Online ISBN: 978-3-030-45691-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)