Author
Listed:
- Shan Dai
- Jiayu Zhang
- Zhelin Huang
- Shipei Zeng
Abstract
Fire safety is a primary concern in safeguarding lives and property. However, it is challenging to predict fire incidents and identify potential influencing factors due to limitations of data, model accuracy and interpretability. This paper proposes a novel scheme designed to enhance predictive and explainable capabilities by integrating multi‐source data, adaptive machine learning methods, and Shapley additive explanation (SHAP) tools for more effective and applicable fire safety management. The scheme shows satisfactory prediction results by leveraging the data from grid‐style management systems and our proposed machine learning method with dynamic time warping distance‐based time series clustering, significantly outperforming the methods merely based on time series modeling. Moreover, clustered features help to clarify the main influencing risk factors and provide clearer insights for model interpretability. With global SHAP, community clusters capturing community fire event frequency, as well as historical records on fire police rescue, smoke alarms, and fire alarms, are found to be significant risk factors among all the features over the whole communities and periods via the model interpretability analysis, implying that communities where fires used to occur frequently are more likely to occur in future, which should be highly vigilant in real fire management. With local SHAP, specific risk factors that vary across communities can be identified for any single community with a given period. We demonstrate the potential of this integrated machine learning scheme in improving the prediction accuracy and risk identification applicability of fire incidents, which contributes to more effective and customized fire safety management.
Suggested Citation
Shan Dai & Jiayu Zhang & Zhelin Huang & Shipei Zeng, 2025.
"Fire Prediction and Risk Identification With Interpretable Machine Learning,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(5), pages 1699-1715, August.
Handle:
RePEc:wly:jforec:v:44:y:2025:i:5:p:1699-1715
DOI: 10.1002/for.3266
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:44:y:2025:i:5:p:1699-1715. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.