Predicting Fire Brigades Operational Breakdowns: A Real Case Study

Cerna, Selene; Guyeux, Christophe; Royer, Guillaume; Chevallier, Céline; Plumerel, Guillaume

doi:10.3390/math8081383

Cited by 16 publications

(19 citation statements)

References 21 publications

(30 reference statements)

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“…Even if the intervention's reason could be an indicator of the call urgency, we did not consider this sensitive attribute in our data analysis nor privacy-preserving prediction models. This is because, for SDIS 25, the ARTs limits are defined by the zone [9]. Additionally, we also did not include the victims' personal data (e.g., gender, age) in our predictions or analysis since, during the calls, the operator may not acquire such information, e.g., when a third party activates the SDIS 25 for unidentified victims.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, although in some countries the reason of the emergency may require a recommended ART [30], for SDIS 25, ART depends on the Zone as detailed in [9]. There are three zones: Z1 refers to urban areas, Z2 refers to semi-urban areas, and Z3 refers to rural ones.…”

Section: Process Flow Descriptionmentioning

confidence: 99%

“…Therefore, SDIS 25 ambulances should arrive on-scene with ART ≤ 10 minutes (min) on Z1 and with ART ≤ 25 min on Z2 and Z3, i.e., including the pre-travel delay (gathering armament) and travel time. If these time limits are not reached, a breakdown in SDIS 25 services is generated [9]. The victim state may also be impacted negatively with high ARTs [1,5].…”

Section: Process Flow Descriptionmentioning

confidence: 99%

“…For instance, the pre-travel delay directly affects ARTs if there is a lack of human and material resources when a call is received. This means, if there is a lack of firefighters, ambulances, or both, ART may be higher than allowed and, hence, a breakdown in the SDIS 25 service occurs [9]. This inability to assist within the time limits impacts negatively both EMS and victims because the safety of a certain area or population will be at risk.…”

Section: Introductionmentioning

confidence: 99%

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Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Arcolezi

Cerna

Guyeux

et al. 2021

MCA

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Emergency medical services (EMS) provide crucial emergency assistance and ambulatory services. One key measurement of EMS’s quality of service is their ambulances’ response time (ART), which generally refers to the period between EMS notification and the moment an ambulance arrives on the scene. Due to many victims requiring care within adequate time (e.g., cardiac arrest), improving ARTs is vital. This paper proposes to predict ARTs using machine-learning (ML) techniques, which could be used as a decision-support system by EMS to allow a dynamic selection of ambulance dispatch centers. However, one well-known predictor of ART is the location of the emergency (e.g., if it is urban or rural areas), which is sensitive data because it can reveal who received care and for which reason. Thus, we considered the ‘input perturbation’ setting in the privacy-preserving ML literature, which allows EMS to sanitize each location data independently and, hence, ML models are trained only with sanitized data. In this paper, geo-indistinguishability was applied to sanitize each emergency location data, which is a state-of-the-art formal notion based on differential privacy. To validate our proposals, we used retrospective data of an EMS in France, namely Departmental Fire and Rescue Service of Doubs, and publicly available data (e.g., weather and traffic data). As shown in the results, the sanitization of location data and the perturbation of its associated features (e.g., city, distance) had no considerable impact on predicting ARTs. With these findings, EMSs may prefer using and/or sharing sanitized datasets to avoid possible data leakages, membership inference attacks, or data reconstructions, for example.

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Section: Discussionmentioning

confidence: 99%

Section: Process Flow Descriptionmentioning

confidence: 99%

Section: Process Flow Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Arcolezi

Cerna

Guyeux

et al. 2021

MCA

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“…Regardless of the data type, it is well known to provide better solutions than other ML algorithms, because of its rapidity, efficiency, and scalability [52,53]. It has been the focus of research in various fields [54][55][56]. In particular, in mechanical machining [52,57,58], XGB is a good choice to predict tool wear and surface roughness.…”

Section: Extreme Gradient Boosting Regression (Xgb)mentioning

confidence: 99%

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

Nguyen

Truong

et al. 2021

Mathematical Problems in Engineering

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This paper deals with the prediction of surface roughness in manufacturing polycarbonate (PC) by applying Bayesian optimization for machine learning models. The input variables of ultraprecision turning—namely, feed rate, depth of cut, spindle speed, and vibration of the X-, Y-, and Z-axis—are the main factors affecting surface quality. In this research, six machine learning- (ML-) based models—artificial neural network (ANN), Cat Boost Regression (CAT), Support Vector Machine (SVR), Gradient Boosting Regression (GBR), Decision Tree Regression (DTR), and Extreme Gradient Boosting Regression (XGB)—were applied to predict the surface roughness (Ra). The predictive performance of the baseline models was quantitatively assessed through error metrics: root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The overall results indicate that the XGB and CAT models predict Ra with the greatest accuracy. In improving baseline models such as XGB and CAT, the Bayesian optimization (BO) is next used to determine their best hyperparameters, and the results indicate that XGB is the best model according to the evaluation metrics. Results have shown that the performance of the models has been improved significantly with BO. For example, the values of RMSE and MAE of XGB have decreased from 0.0076 to 0.0047 and from 0.0063 to 0.0027, respectively, for the training dataset. Using the testing dataset, the values of RMSE and MAE of XGB have decreased from 0.4033 to 0.2512 and from 0.2845 to 0.2225, respectively. Moreover, the vibrations of the X, Y, and Z axes and feed rate are the most significant feature in predicting the results, which is in high accordance with the literature. We find that, in a specified value domain, the vibration of the axes has a greater influence on the surface quality than does the cutting condition.

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How to Build an Optimal and Operational Knowledge Base to Predict Firefighters’ Interventions

Guyeux

Makhoul

Bahi

2022

Lecture Notes in Networks and Systems

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It has recently been shown that pre-emergency transport, whether performed by firefighters or private ambulances, has a predictive character due to the fact that rescue is directly related to human activity, which is itself predictable. XGBoost has emerged as the best tool to predict the number of interventions by type, but how to design an optimal and operational knowledge base has not been discussed so far. We propose to explain how to make such a base with a content that is both relevant and can be continuously updated, making possible the industrialization of the process, and thus a better operational response of the concerned services. We show that three feature selection tools custom-built for XGBoost are mature enough to allow the optimization of such a database, and a good accuracy in predictions. We also show what these tools can bring in terms of business knowledge, and discuss the organizational and efficiency consequences that such an optimized predictive model could bring.

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Predicting Fire Brigades Operational Breakdowns: A Real Case Study

Cited by 16 publications

References 21 publications

Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

How to Build an Optimal and Operational Knowledge Base to Predict Firefighters’ Interventions

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