Predicting non-residential building fire risk using geospatial information and convolutional neural networks

Anderson-Bell, Jake; Schillaci, Calogero; Lipani, Aldo

doi:10.1016/j.rsase.2021.100470

Cited by 12 publications

(6 citation statements)

References 22 publications

(16 reference statements)

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“…With emerging information technology innovations [ 1 ] such as the Internet of Things (IoT) and Big Data, among others, machine learning is becoming increasingly important in assessing building fire risk and improving risk warning capabilities. Machine learning is relevant to various types of risk assessment, such as community-level building fire prediction (e.g., real estate [ 2 ], parking lots [ 3 ], public spaces, commercial complexes, and shopping centers [ 4 ]), property-level building fire assessment (using assessment metrics such as property damage [ 5 ], casualties [ 6 ], and incident severity [ 7 , 8 ]), and regional risk analysis [ 9 ]. Fire risk assessment of stadiums is one of the hot research topics in the field of fire safety.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums

Fan

Zhang

et al. 2023

Sensors

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Machine learning methods can establish complex nonlinear relationships between input and response variables for stadium fire risk assessment. However, the output of machine learning models is considered very difficult due to their complex “black box” structure, which hinders their application in stadium fire risk assessment. The SHapley Additive exPlanations (SHAP) method makes a local approximation to the predictions of any regression or classification model so as to be faithful and interpretable, and assigns significant values (SHAP value) to each input variable for a given prediction. In this study, we designed an indicator attribute threshold interval to classify and quantify different fire risk category data, and then used a random forest model combined with SHAP strategy in order to establish a stadium fire risk assessment model. The main objective is to analyze the impact analysis of each risk characteristic on four different risk assessment models, so as to find the complex nonlinear relationship between risk characteristics and stadium fire risk. This helps managers to be able to make appropriate fire safety management and smart decisions before an incident occurs and in a targeted manner to reduce the incidence of fires. The experimental results show that the established interpretable random forest model provides 83% accuracy, 86% precision, and 85% recall for the stadium fire risk test dataset. The study also shows that the low level of data makes it difficult to identify the range of decision boundaries for Critical mode and Hazardous mode.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums

Fan

Zhang

et al. 2023

Sensors

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“…The forest fire risk monitoring methods proposed by most scholars are for specific scenes, with several problems that need to be solved. How to recognize the color, form, and texture of fire in different chaotic backgrounds is the key to forest fire risk recognition [22][23][24][25][26]. The purpose of this study was to effectively extract the complex upper layer features of fire risk images and improve the robustness of input conversion by constructing an unmanned aerial vehicle (UAV) image-based forest fire risk prediction model based on a deep learning back-propagation neural network.…”

Section: Introductionmentioning

confidence: 99%

A Highly Accurate Forest Fire Prediction Model Based on an Improved Dynamic Convolutional Neural Network

et al. 2022

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In this work, an improved dynamic convolutional neural network (DCNN) model to accurately identify the risk of a forest fire was established based on the traditional DCNN model. First, the DCNN network model was trained in combination with transfer learning, and multiple pre-trained DCNN models were used to extract features from forest fire images. Second, principal component analysis (PCA) reconstruction technology was used in the appropriate subspace. The constructed 15-layer forest fire risk identification DCNN model named “DCN_Fire” could accurately identify core fire insurance areas. Moreover, the original and enhanced image data sets were used to evaluate the impact of data enhancement on the model’s accuracy. The traditional DCNN model was improved and the recognition speed and accuracy were compared and analyzed with the other three DCNN model algorithms with different architectures. The difficulty of using DCNN to monitor forest fire risk was solved, and the model’s detection accuracy was further improved. The true positive rate was 7.41% and the false positive rate was 4.8%. When verifying the impact of different batch sizes and loss rates on verification accuracy, the loss rate of the DCN_Fire model of 0.5 and the batch size of 50 provided the optimal value for verification accuracy (0.983). The analysis results showed that the improved DCNN model had excellent recognition speed and accuracy and could accurately recognize and classify the risk of a forest fire under natural light conditions, thereby providing a technical reference for preventing and tackling forest fires.

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“…Anderson-Bell, J. [14] constructed a framework for predicting fire risk in buildings in London. It uses Fire Brigade incident data, aerial imagery, and a digital surface model (DSN).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Fire Risk Classification Prediction of Stadiums: Multi-Dimensional Machine Learning Analysis Based on Intelligent Perception

Fan

Zhao

et al. 2022

Applied Sciences

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Stadium fires can easily cause massive casualties and property damage. The early risk prediction of stadiums will be able to reduce the incidence of fires by making corresponding fire safety management and decision making in an early and targeted manner. In the field of building fires, some studies apply data mining techniques and machine learning algorithms to the collected risk hazard data for fire risk prediction. However, most of these studies use all attributes in the dataset, which may degrade the performance of predictive models due to data redundancy. Furthermore, machine learning algorithms are numerous and applied to fewer stadium fires, and it is crucial to explore models suitable for predicting stadium fire risk. The purpose of this study was to identify salient features to build a model for predicting stadium fire risk predictions. In this study, we designed an index attribute threshold interval to classify and quantify different fire risk data. We then used Gradient Boosting-Recursive Feature Elimination (GB-RFE) and Pearson correlation analysis to perform efficient feature selection on risk feature attributes to find the most informative salient feature subsets. Two cross-validation strategies were employed to address the dataset imbalance problem. Using the smart stadium fire risk data set provided by the Wuhan Emergency Rescue Detachment, the optimal prediction model was obtained based on the identified significant features and six machine learning methods of 12 combination forms, and full features were input as an experimental comparison study. Five performance evaluation metrics were used to evaluate and compare the combined models. Results show that the best performing model had an F1 score of 81.9% and an accuracy of 93.2%. Meanwhile, by introducing a precision-recall curve to explain the actual classification performance of each model, AdaBoost achieves the highest Auprc score (0.78), followed by SVM (0.77), which reveals more stable performance under such imbalanced data.

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Predicting non-residential building fire risk using geospatial information and convolutional neural networks

Cited by 12 publications

References 22 publications

Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums

Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums

A Highly Accurate Forest Fire Prediction Model Based on an Improved Dynamic Convolutional Neural Network

Dynamic Fire Risk Classification Prediction of Stadiums: Multi-Dimensional Machine Learning Analysis Based on Intelligent Perception

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