Firefighter Intervention Predictive Modeling: Reliability Assessment

Sirri, Naoufal; Guyeux, Christophe

doi:10.1109/iccsc62074.2024.10617206

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2024

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Cited by 2 publications

References 12 publications

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Factor Analysis of Weather Conditions Impact on Firefighter Interventions

Sirri,

Guyeux

2024

Communications in Computer and Information Science

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Factor Analysis of Weather Conditions Impact on Firefighter Interventions

Sirri,

Guyeux

2024

Communications in Computer and Information Science

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A Hybrid Ontology-Based Feature SelectionFramework for Enhancing Predictive Accuracy inRegression Models

Ayad,

Mallouhy,

Guyeux

2024

Preprint

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Predicting firefighter interventions presents a complex challenge due to the high dimensionality and intricacy of the data. While machine learning (ML) technologies offer promising solutions, ineffective feature selection can significantly hinder model performance and reduce predictive accuracy. This study proposes a hybrid feature selection approach that combines ontology-based reasoning with traditional ML techniques to enhance the predictive accuracy of regression models for firefighter interventions. We utilized three machine learning algorithms—XGBoost, LightGBM, and Long Short-Term Memory (LSTM) networks—across two feature selection strategies: one solely based on ML algorithms, and another using a hybrid approach that integrates ontology-based centrality metrics, such as degree, closeness, and betweenness, with ML techniques. A domain-specific ontology was developed to capture key environmental, temporal, and intervention-related factors, improving the feature selection process for more interpretable and contextually relevant features. The results clearly show that the hybrid feature selection approach consistently outperforms the ML-only method. For the XGBoost model, the hybrid approach resulted in an R2 of 0.976, compared to 0.97 for the ML-only method. The LSTM model also saw improvements, with the hybrid approach achieving an R2 of 0.964, compared to 0.96 for ML-only. Similarly, for the LightGBM model, the hybrid approach produced an R2 of 0.975, compared to 0.97 for ML-only. This research underscores the significant advantages of combining ontology-based feature selection with ML, leading to improved predictive accuracy and better model interpretability, particularly in high-dimensional data environments.

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Firefighter Intervention Predictive Modeling: Reliability Assessment

Cited by 2 publications

References 12 publications

Factor Analysis of Weather Conditions Impact on Firefighter Interventions

Factor Analysis of Weather Conditions Impact on Firefighter Interventions

A Hybrid Ontology-Based Feature SelectionFramework for Enhancing Predictive Accuracy inRegression Models

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