Forensic and Cause-and-Effect Analysis of Fire Safety in the Republic of Serbia: An Approach Based on Data Mining
Abstract
1. Introduction
2. Materials and Methods
2.1. Dataset
2.1.1. Input Variables
- “Cause of Fire (CoF)” variable contains five groups of values, labeled A, B, C, D, and E, which identify the causes that led to the fire, such as human factors, technical factors, or natural causes. The method of grouping different causes of fires is based on the classification carried out in the following Section 3, while its detailed structure is shown in Table 3.
- The variable “Fire Location (FL)” is classified into three groups, and it has a similar function as the “FCL” variable. Additionally, this variable highlights more detailed information about the location of the fire in residential buildings, for example, fire on the ground floor, fire on the second floor, etc. The structure of the individual values of this variable is shown in Table 4.
- The variable “Fire Category by Location (FCL)” also contains four groups, representing information on the fire location (open space/indoor space) and building type. The detailed structure of the individual values of this variable is shown in Table 5.
- The “Season” variable is classified into four groups; the data indicate the susceptibility of the season and weather conditions to the occurrence of fires.
- The variable “Day of Day (DoD)” is classified into two groups; the data indicate the fires during weekdays and during weekends.
- The variable “City of Fire Origin (CFO)” is used for spatial data classification, critical location identification, and geographic fire analysis. The values of this variable are divided into eight groups, using Quantum Geographic Information System (QGIS) software [18]. These groups are presented in the form of maps in Figure A1 and Figure A2 of Appendix A.
- The variable “Year of Fire Occurrence (YFO)” serves for the temporal classification of the data, the values of which are analyzed in annual intervals.
- “Hour of Notification (HoN)” variable is used for the temporal classification of data, the values of which are observed in the number and frequency of fire alarms in certain time intervals.
- “Alert/Arrival (A/A)” variable identifies the evaluation of the effectiveness of the fire response. It shows the time interval between receiving a fire notification and the arrival of the fire department at the location.
- “Alert/Extinguishment (A/E)” variable identifies the interval from the time the fire department arrives at the location to the time the fire is extinguished.
2.1.2. Output Variables
2.2. Data Segmentation (Clustering)
2.2.1. Silhouette Score Method
- -
- is the average distance of the point from all other points within its cluster (compactness),
- -
- is the average distance to points in the nearest other cluster (separability).
2.2.2. K-Means Algorithm
- Initialization step: initial centroid points are (randomly) selected.
- Assignment step: clusters are formed based on the “proximity” of each point , to the nearest centroid , . In other words, for a given dataset , the algorithm seeks a partition into clusters that minimizes the distance which is usually measured as the Euclidean, or some other similarity metrics. In this way, each point is assigned to the cluster whose centroid is closest .
- Update step: New centroids are calculated as the midpoints in each cluster.
- Repeat steps 2 and 3 until the centroid changes.
2.2.3. Agglomerative-Hierarchical (AH) Clustering
- step: Start with n clusters (each point is its own cluster).
- step: Calculate the distances between all clusters.
- step: Merge the two closest clusters into a new one.
- step: Update the distance between the clusters.
- step: Repeat steps 3 and 4 until the given number of clusters is formed.
2.3. Data Classification (Decision Trees)
2.3.1. Basic Principles
- -
- Selection of the best attribute for division (based on entropy, Gini index or statistical tests);
- -
- Branching and continuing the division until the stopping criterion is met (when all cases are within the same class or the maximum depth of the tree is reached).
2.3.2. CHAID Algorithm
3. Results
3.1. Stochastic Modeling
- -
- Wilcoxon rank sum test with continuity correction (W);
- -
- permutation asymptotic general independence test (Z);
- -
- asymptotic two-sample Kolmogorov–Smirnov test (D).
3.2. Data Clustering
- For the K-means clustering, the SS values gradually increase from to , reaching a plateau between and , where they become identical.
- Similarly, for the AH clustering, the SS values increases rapidly up to , while there is no further improvement from to .
3.2.1. K-Means Clustering
- -
- Cluster C: higher number of injuries, almost no fatalities.
- -
- Cluster D: serious fireplaces (average of almost 10 injured).
- -
- Cluster E: transition between mild and serious fires (“yellow alert”).
- -
- Clusters F and G: rare but extreme fires with a high number of victims and injuries.
- -
- Cluster H: very specific accidents with few injuries and high mortality.
- -
- Cluster I: mixed profile with 1–2 injured and fatalities.
3.2.2. AH Clustering
- -
- Cluster A contains the most severe incidents, with an average of 21 injuries and 4.67 deaths. These are typically fires in large facilities (hospitals, factories), that is, mass tragedies. It should also be noted that this cluster has high variability in the number of injuries and fatalities (columns StDev in Table 11) and is equivalent to cluster G in K-means clustering.
- -
- Cluster B, with an average of approximately 3.35 injuries and almost no deaths, shows medium-severity fires with successful evacuation.
- -
- Cluster C (~10.5 injuries, ~0.67 deaths) indicates a very serious situation and a potential delay in the response of the competent services.
- -
- Cluster D (~5.8 injuries, 0 deaths) represents moderately risky events, probably fires in schools, business premises, etc.
- -
- Cluster E (~1.2 injuries, 1 death) has mixed outcomes. These are most likely possible accidents with partial evacuations.
- -
- Cluster F (~0.18 injured, ~2.1 deaths) shows extremely deadly fires, which typically becomes indoors.
- -
- Cluster G (~2.0 injured, 0 deaths) are typical smaller fires with minor injuries.
- -
- Cluster H (~1.0 injured, 0 deaths) represents the most common pattern, i.e., smaller fires with one injured.
- -
- Cluster I (~2.25 injured, ~5.25 deaths) indicates catastrophic fires with high mortality, which are quite rare, but very serious.
- -
- Cluster J (0 injured, 1 deaths) contains data on fires with fatalities and no injuries. It is mostly about explosions, sudden incidents, etc.
3.3. Classification Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
- The Figure A3 shows the decision tree for cause D and the target variable “Injuries”.
- The Figure A4 shows the decision tree for cause D and the target variable “Fatalities”.
References
- Butry, D.T.; Prestemon, J.P.; Abt, K.L.; Sutphen, R. Economic Optimization of Wildfire Intervention Activities. Forest Policy Econ. 2010, 12, 115–121. [Google Scholar] [CrossRef]
- Zou, Y.; Rasch, P.J.; Wang, H.; Xie, Z.; Zhang, R. Increasing Large Wildfires over the Western United States Linked to Diminishing Sea Ice in the Arctic. Nat. Commun. 2021, 12, 6048. [Google Scholar] [CrossRef]
- Madaio, M.; Chen, S.T.; Haimson, O.L.; Zhang, W.; Cheng, X.; Hinds-Aldrich, M.; Chau, D.H.; Dilkina, B. Firebird: Predicting Fire Risk and Prioritizing Fire Inspections in Atlanta. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016. [Google Scholar]
- Choi, J.; Yun, Y.; Chae, H. Forest Fire Risk Prediction in South Korea Using Google Earth Engine: Comparison of Machine Learning Models. Land 2025, 14, 1155. [Google Scholar] [CrossRef]
- Sherry, L.; Chaudhari, M. Aerial Fire Fighting Operational Statistics (2024): Very Large/Large Air Tankers. Fire 2025, 8, 160. [Google Scholar] [CrossRef]
- Gündüz, H.İ.; Torun, A.T.; Gezgin, C. Post-Fire Burned Area Detection Using Machine Learning and Burn Severity Classification with Spectral Indices in İzmir: A SHAP-Driven XAI Approach. Fire 2025, 8, 121. [Google Scholar] [CrossRef]
- Alkhatib, R.; Sahwan, W.; Alkhatieb, A.; Schütt, B. A Brief Review of Machine Learning Algorithms in Forest Fires Science. Appl. Sci. 2023, 13, 8275. [Google Scholar] [CrossRef]
- Abid, F. A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems. Fire Technol. 2021, 57, 559–590. [Google Scholar] [CrossRef]
- Rubí, J.N.S.; Paulo de Carvalho, H.P.; Paulo, R.L.G. Application of Machine Learning Models in the Behavioral Study of Forest Fires in the Brazilian Federal District region. Eng. Appl. Artif. Intell. 2023, 118, 105649. [Google Scholar] [CrossRef]
- Jain, P.; Coogan, S.C.; Subramanian, S.G.; Crowley, M.; Taylor, S.; Flannigan, M.D. A Review of Machine Learning Applications in Wildfire Science and Management. Environ. Rev. 2020, 28, 478–505. [Google Scholar] [CrossRef]
- Sun, L.; Xu, C.; He, Y.; Zhao, Y.; Xu, Y.; Rui, X.; Xu, H. Adaptive Forest Fire Spread Simulation Algorithm Based on Cellular Automata. Forests 2021, 12, 1431. [Google Scholar] [CrossRef]
- Wood, D.A. Prediction and Data Mining of Burned Areas of Forest Fires: Optimized Data Matching and Mining Algorithm Provides Valuable Insight. Artif. Intell. Agric. 2021, 5, 24–42. [Google Scholar] [CrossRef]
- McNorton, J.R.; Di Giuseppe, F.; Pinnington, E.; Chantry, M.; Barnard, C. A Global Probability-of-Fire (PoF) Forecast. Geophys. Res. Lett. 2024, 51, e2023GL107929. [Google Scholar] [CrossRef]
- Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed.; Springer: New York, NY, USA, 2009. [Google Scholar] [CrossRef]
- Aggarwal, C. Data Mining: The Textbook; Springer International Publishing AG: Cham, Switzerland, 2015. [Google Scholar] [CrossRef]
- The CTIF World Fire Statistics, Report № 29. 2024. Available online: https://www.ctif.org/world-fire-statistics (accessed on 13 July 2025).
- Marić, P.; Mlađan, D.; Stevanović, B.; Nikolić, G.; Đukanović, S. Statistical Approach for Establishing Individual Fire Risk in European Countries and Republic of Serbia (in Serbian). In Proceedings of the ISC: Safety Engineering; Fire and Explosion Protection, Novi Sad, Serbia, 26–27 September 2018; pp. 125–134. [Google Scholar]
- Quantum Geographic Information System (QGIS), Software. Available online: https://qgis.org/ (accessed on 13 June 2025).
- Stojanović, V.; Ljajko, E.; Tošić, M. Parameters Estimation in Non-Negative Integer-Valued Time Series: Approach Based on Probability Generating Functions. Axioms 2023, 12, 112. [Google Scholar] [CrossRef]
- Stojanović, V.S.; Bakouch, H.S.; Gajtanović, Z.; Almuhayfith, F.E.; Kuk, K. Integer-Valued Split-BREAK Process with a General Family of Innovations and Application to Accident Count Data Modeling. Axioms 2024, 13, 40. [Google Scholar] [CrossRef]
- Xu, R.; Wunsch, D. Survey of Clustering Algorithms. IEEE Trans. Neural Netw. 2005, 16, 645–678. [Google Scholar] [CrossRef]
- De Ville, B. Decision trees. Wiley Interdiscip. Rev. Comput. Stat. 2013, 5, 448–455. [Google Scholar] [CrossRef]
- Myles, A.J.; Feudale, R.N.; Liu, Y.; Woody, N.A.; Brown, S.D. An Introduction to Decision Tree Modeling. J. Chemom. 2004, 18, 275–285. [Google Scholar] [CrossRef]
- Ritschard, G. CHAID and Earlier Supervised Tree Methods. In Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences; Routledge: New York, NY, USA, 2013; pp. 48–74. [Google Scholar]
- Saito, M.Y.; Rodrigues, J. A Bayesian Analysis of Zero and One Inflated Distributions. Rev. Mat. Estatíst. 2005, 23, 47–57. [Google Scholar]
- Zhang, C.; Tian, G.; Ng, K. Properties of the Zero-and-One Inflated Poisson Distribution and Likelihood-Based Inference Methods. Stat. Interface 2016, 9, 11–32. [Google Scholar] [CrossRef]
- Zhang, C.; Tian, G.-L.; Yuen, K.C.; Wu, Q.; Li, T. Multivariate Zero-and-One Inflated Poisson Model with Applications. J. Comput. Appl. Math. 2020, 365, 112356. [Google Scholar] [CrossRef]
- Qi, X.; Li, Q.; Zhu, F. Modeling Time Series of Count with Excess Zeros and Ones Based on INAR(1) Model with Zero-and-One Inflated Poisson Innovations. J. Comput. Appl. Math. 2019, 346, 572–590. [Google Scholar] [CrossRef]
- Mohammadi, Z.; Sajjadnia, Z.; Bakouch, H.S.; Sharafi, M. Zero-and-One Inflated Poisson–Lindley INAR(1) Process for Modelling Count Time Series with Extra Zeros and Ones. J. Stat. Comput. Simulat. 2022, 92, 2018–2040. [Google Scholar] [CrossRef]
- Stojanović, V.S.; Bakouch, H.S.; Ljajko, E.; Qarmalah, N. Zero-and-One Integer-Valued AR(1) Time Series with Power Series Innovations and Probability Generating Function Estimation Approach. Mathematics 2023, 11, 1772. [Google Scholar] [CrossRef]
- Weiß, C.H.; Homburg, A.; Puig, P. Testing for Zero Inflation and Overdispersion in INAR(1) Models. Stat. Pap. 2019, 60, 823–848. [Google Scholar] [CrossRef]
- Dowd, C. Twosamples: Fast Permutation Based Two Sample Tests, R Package, Version 2.0.1. 2023. Available online: https://cloud.r-project.org/web/packages/twosamples/index.html (accessed on 25 May 2025).
- Scikit-Learn Documentation. Available online: https://scikit-learn.org/stable/modules/clustering.html (accessed on 17 April 2025).
- Baizyldayeva, U.B.; Uskenbayeva, R.K.; Amanzholova, S.T. Decision Making Procedure: Applications of IBM SPSS Cluster Analysis and Decision Tree. World Appl. Sci. J. 2013, 21, 1207–1212. [Google Scholar]
- Abdalla, H.I. A Brief Comparison of K-means and Agglomerative Hierarchical Clustering Algorithms on Small Datasets. Proceeding of the 2021 International Conference on Wireless Communications, Networking and Applications, Berlin, Germany, 17–19 December 2021; WCNA 2021 Lecture Notes in Electrical Engineering. Qian, Z., Jabbar, M., Li, X., Eds.; Springer: Singapore, 2021. [Google Scholar] [CrossRef]
- Peterson, A.D.; Ghosh, A.P.; Maitra, R. Merging K-means with Hierarchical Clustering for Identifying General-Shaped Groups. Stat (Int. Stat. Inst.) 2018, 7, e172. [Google Scholar] [CrossRef]
- Pireddu, A.; Bedini, A.; Lombardi, M.; Ciribini, A.L.C.; Berardi, D. A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management. Int. J. Environ. Res. Public. Health 2024, 21, 831. [Google Scholar] [CrossRef]
- Linardos, V.; Drakaki, M.; Tzionas, P.; Karnavas, Y. Machine Learning in Disaster Management: Recent Developments in Methods and Applications. Mach. Learn. Knowl. Extract. 2022, 4, 446–473. [Google Scholar] [CrossRef]
Country | Deaths per 100,000 Inh. | Injuries per 100,000 Inh. |
---|---|---|
Bulgaria | 2.39 | 4.4 |
Croatia | 0.88 | 4.1 |
Finland | 0.92 | 6.6 |
Greece | 0.67 | 0.9 |
Hungary | 0.97 | 8.0 |
Portugal | 0.52 | 13.2 |
Serbia | 1.50 | 4.9 |
Average | 1.14 | 3.2 |
Ord. Num. | Variable (Attribute) | Type | Values | Description | ||
---|---|---|---|---|---|---|
Injuries | Fatalities | |||||
1. | Cause of the Fire (CoF) | Nominal | A, B, C, D, E | The various causes of fires. | 0.0602 | 0.0562 |
2. | Fire Location (FL) | Nominal | Position I–III | Positions of fire locations. | 0.0212 | 0.0197 |
3. | Fire Category by Location (FCL) | Nominal | Category I–IV | The various category of fires. | 0.0230 | 0.0227 |
4. | Season | Nominal | Winter, Spring, Summer, Automn | Season of the fire. | 0.0086 | 0.0090 |
5. | Day of Day (DoD) | Nominal | Weekday, Weekend | Day of the week when the fire occurred. | 0.0004 | 0.0002 |
6. | City of Fire Origin (CFO) | Nominal | Group I–VIII | City where the fire occurred. | 0.0086 | 0.0075 |
7. | Year of Fire Occurrence (YFO) | Ordinal | 2009, …, 2023 | The year the fire occurred. | 0.0040 | 0.0044 |
8. | Hour of Notification (HoN) | Ordinal | 1, …, 24 [h] | The ordinal number of hour of the day when the fire occurred. | 0.0082 | 0.0094 |
9. | Alert/Arrival (A/A) | Numeric | 0, …, 186 [min] | Time from notification to arrival of the fire service. | 0.0063 | 0.0055 |
10. | Alert/Extinguishment (A/E) | Numeric | 0, …, 1432 [min] | Time from notification to extinguishing the fire. | 0.0152 | 0.0187 |
Values | Cause of the Fire | Number of Cases |
---|---|---|
A | Unspecified; Electrical conductors; Other causes; Collision; Exothermic reaction | 3504 |
B | Explosion; Friction; Damage-defects | 268 |
C | Open flames; Construction defects; Fireplaces; Conductors overheating from overload; Electrical devices | 1619 |
D | Cigarette butt | 272 |
E | Welding; Natural occurrences; Grinding; Self-ignition; Static electricity | 106 |
Values | Fire Location | Number of Cases |
---|---|---|
Position I | Ground floor | 2758 |
Position II | Basement-basement, Floor from 1st to 4th, Attic, Attic-roof | 1532 |
Position III | Floors from 4th to 7th, Floors from 8th to 15th, Floors higher than 16th, High attic, Unspecified | 1479 |
Values | Fire Category (by Location) | Number of Cases |
---|---|---|
Category I | Residential building; Residential and commercial building; Trade and craft shop; Catering facility; Office building; Religious object; Health institution; Kindergarten/school/faculty; Hotel/motel; Cinema/theater; Nursing home; Department store; Home for neglected children | 3502 |
Category II | Bus; Tanker trucks; Road. vehicle. Other road vehicles; Freight road vehicle; Agricultural machines; Passenger car; Freight wagon; Water transportation vehicle; Electric locomotive; Means of air transport; Diesel locomotive | 562 |
Category III | Coniferous forest; Orchard; Macchia (low vegetation); Mixed forest; Meadow; Other open space; Cereals; Deciduous forest; Vineyard | 716 |
Category IV | Barrack/shed; Agricultural building; Other civil. facilities; Garbage dump; Container; Sil; Parking lot-garage; Production plant; Transformer station; Warehouses; Chimney; Construction site; Gas plant; Construction machinery; Working machinery; Refinery | 989 |
Variable | Min | 25% | 50% | 75% | Max | Mode | Mean | Var | StDev | Skew | Sum |
---|---|---|---|---|---|---|---|---|---|---|---|
Injuries (X) | 0 | 1 | 1 | 1 | 30 | 1 | 1.042 | 1.041 | 1.020 | 7.862 | 6013 |
Fatalities (Y) | 0 | 0 | 0 | 0 | 7 | 0 | 0.237 | 0.238 | 0.488 | 2.855 | 1370 |
Variable | 0-Count | (p-Values) | 1-Count | (p-Values) | ||
---|---|---|---|---|---|---|
Injuries () | 1120 | 0.1941 | −0.5046 (0.6931) | 3840 | 0.6656 | 1.6819 * () |
Fatalities () | 4507 | 0.7812 | 1.7203 * () | 1183 | 0.2051 | 1.5250 (0.0636) |
Distribution | Parameters | Statistics/(p-Values) | ||||
---|---|---|---|---|---|---|
W | Z | D | ||||
Poisson | 1.0423 | (3.74 ) | 1.8472 (0.0647) | (~0.0000) | ||
1-Poisson | 1.0423 | 0.5009 | 0.4991 | 16,695,024 (0.6712) | 0.3409 (0.7332) | 0.0104 (0.9139) |
Distribution | Parameters | Statistics/(p-Values) | ||||
---|---|---|---|---|---|---|
W | Z | D | ||||
Poisson | 0.2375 | 16,539,970 (0.4340) | −1.5793 (0.1143) | 0.0154 (0.4984) | ||
0-Poisson | 0.2394 | 0.0813 | 0.9187 | 16,662,252 (0.8663) | 1.0421 (0.2793) | 0.0106 (0.9037) |
Cluster | Injuries | Fatalities | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Label | Count | Mean | StDev | Min | 25% | 50% | 75% | Max | Mean | StDev | Min | 25% | 50% | 75% | Max |
A | 3745 | 1.000 | 0.000 | 1.0 | 1.00 | 1.0 | 1.00 | 1.0 | 0.000 | 0.000 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 |
B | 1060 | 0.000 | 0.000 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 1.000 | 0.000 | 1.0 | 1.00 | 1.0 | 1.00 | 1.0 |
C | 101 | 4.693 | 0.956 | 4.0 | 4.00 | 4.0 | 5.00 | 7.0 | 0.089 | 0.349 | 0.0 | 0.00 | 0.0 | 0.00 | 2.0 |
D | 12 | 9.917 | 1.929 | 8.0 | 8.75 | 10.0 | 10.25 | 15.0 | 0.500 | 1.000 | 0.0 | 0.00 | 0.0 | 0.25 | 3.0 |
E | 659 | 2.184 | 0.387 | 2.0 | 2.00 | 2.0 | 2.00 | 3.0 | 0.000 | 0.000 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 |
F | 4 | 2.250 | 2.062 | 0.0 | 1.50 | 2.0 | 2.75 | 5.0 | 5.250 | 0.957 | 4.0 | 4.75 | 5.5 | 6.00 | 6.0 |
G | 3 | 21.00 | 8.185 | 14.0 | 16.50 | 19.0 | 24.50 | 30.0 | 4.667 | 2.082 | 3.0 | 3.50 | 4.0 | 5.50 | 7.0 |
H | 66 | 0.136 | 0.426 | 0.0 | 0.00 | 0.0 | 0.00 | 2.0 | 2.106 | 0.310 | 2.0 | 2.00 | 2.0 | 2.00 | 3.0 |
I | 119 | 1.303 | 0.590 | 1.0 | 1.00 | 1.0 | 1.00 | 4.0 | 1.017 | 0.129 | 1.0 | 1.00 | 1.0 | 1.00 | 2.0 |
Cluster | Injuries | Fatalities | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Label | Count | Mean | StDev | Min | 25% | 50% | 75% | Max | Mean | StDev | Min | 25% | 50% | 75% | Max |
A | 3 | 21.00 | 8.185 | 14.0 | 16.5 | 19.0 | 24.5 | 30.0 | 4.667 | 2.082 | 3.0 | 3.50 | 4.0 | 5.5 | 7.0 |
B | 187 | 3.348 | 0.521 | 3.0 | 3.0 | 3.0 | 4.00 | 6.0 | 0.080 | 0.326 | 0.0 | 0.00 | 0.0 | 0.0 | 2.0 |
C | 9 | 10.56 | 1.810 | 9.0 | 10.0 | 10.0 | 11.0 | 15.0 | 0.667 | 1.118 | 0.0 | 0.00 | 0.0 | 1.0 | 3.0 |
D | 43 | 5.837 | 0.949 | 5.0 | 5.0 | 6.0 | 6.00 | 8.0 | 0.000 | 0.000 | 0.0 | 0.00 | 0.0 | 0.0 | 0.0 |
E | 113 | 1.204 | 0.404 | 1.0 | 1.0 | 1.0 | 1.00 | 2.0 | 1.000 | 0.000 | 1.0 | 1.00 | 1.0 | 1.0 | 1.0 |
F | 67 | 0.179 | 0.548 | 0.0 | 0.0 | 0.0 | 0.00 | 3.0 | 2.104 | 0.308 | 2.0 | 2.00 | 2.0 | 2.0 | 3.0 |
G | 538 | 2.000 | 0.000 | 2.0 | 2.0 | 2.0 | 2.00 | 2.0 | 0.000 | 0.000 | 0.0 | 0.00 | 0.0 | 0.0 | 0.0 |
H | 3745 | 1.000 | 0.000 | 1.0 | 1.0 | 1.0 | 1.00 | 1.0 | 0.000 | 0.000 | 0.0 | 0.00 | 0.0 | 0.0 | 0.0 |
I | 4 | 2.250 | 2.061 | 0.0 | 1.5 | 2.0 | 2.75 | 5.0 | 5.250 | 0.957 | 4.0 | 4.75 | 5.5 | 6.0 | 6.0 |
J | 1060 | 0.000 | 0.000 | 0.0 | 0.0 | 0.0 | 0.00 | 0.0 | 1.000 | 0.000 | 1.0 | 1.00 | 1.0 | 1.0 | 1.0 |
Tree Metric | Cause C | Cause D | ||
---|---|---|---|---|
Injuries | Fatalities | Injuries | Fatalities | |
Total nodes | 120 | 139 | 53 | 52 |
Terminal nodes | 78 | 91 | 35 | 33 |
Tree depth | 7 | 6 | 7 | 7 |
Input variables | 7 | 8 | 6 | 6 |
Estimate | 0.035 | 0.048 | 0.007 | 0.007 |
Std. Error | 0.005 | 0.006 | 0.005 | 0.005 |
Observed | Predicted | |||||
---|---|---|---|---|---|---|
Injuries | Fatalities | |||||
YES | NO | Total | YES | NO | Total | |
YES | 976 | 28 | 1004 | 381 | 39 | 420 |
NO | 20 | 366 | 386 | 28 | 942 | 970 |
Total | 996 | 394 | 1390 | 409 | 981 | 1390 |
Observed | Predicted | |||||
---|---|---|---|---|---|---|
Injuries | Fatalities | |||||
YES | NO | Total | YES | NO | Total | |
YES | 123 | 0 | 123 | 158 | 2 | 160 |
NO | 2 | 147 | 149 | 0 | 112 | 112 |
Total | 125 | 147 | 272 | 158 | 114 | 272 |
Measure | Cause C | Cause D | ||
---|---|---|---|---|
Injuries | Fatalities | Injuries | Fatalities | |
Accuracy | 0.9655 | 0.9518 | 0.9926 | 0.9926 |
Precision | 0.9799 | 0.9315 | 0.9840 | 1.0000 |
Sensitivity | 0.9721 | 0.9071 | 1.0000 | 0.9875 |
Specificity | 0.9482 | 0.9711 | 0.9866 | 1.0000 |
F-measure | 0.9760 | 0.9192 | 0.9919 | 0.9937 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Mitrović, N.; Stojanović, V.S.; Jovanović, M.; Mladjan, D. Forensic and Cause-and-Effect Analysis of Fire Safety in the Republic of Serbia: An Approach Based on Data Mining. Fire 2025, 8, 302. https://doi.org/10.3390/fire8080302
Mitrović N, Stojanović VS, Jovanović M, Mladjan D. Forensic and Cause-and-Effect Analysis of Fire Safety in the Republic of Serbia: An Approach Based on Data Mining. Fire. 2025; 8(8):302. https://doi.org/10.3390/fire8080302
Chicago/Turabian StyleMitrović, Nikola, Vladica S. Stojanović, Mihailo Jovanović, and Dragan Mladjan. 2025. "Forensic and Cause-and-Effect Analysis of Fire Safety in the Republic of Serbia: An Approach Based on Data Mining" Fire 8, no. 8: 302. https://doi.org/10.3390/fire8080302
APA StyleMitrović, N., Stojanović, V. S., Jovanović, M., & Mladjan, D. (2025). Forensic and Cause-and-Effect Analysis of Fire Safety in the Republic of Serbia: An Approach Based on Data Mining. Fire, 8(8), 302. https://doi.org/10.3390/fire8080302