10.57647/AP.2026.1001.02

Modeling Anthropogenic Urban Fire Risk and Its Environmental Pollution Implications Using LR–WLC Approaches: A Case Study of Kashan, Iran

PDF
  • Mohammad Amin Vakilalroaya1,
  • Saeed Malmasi*1,
  • Mojgan Zaeimdar1,
  • Mahnaz Mirza Ebrahim Tahrani1,
  1. Department of Environment, NT.C., Islamic Azad University, Tehran, Iran

Received: 2025-11-17

Revised: 2026-02-01

Accepted: 2026-02-16

Published in Issue 2026-06-30

Copyright (c) 2026 Mohammad Amin Vakilalroaya, Saeed Malmasi, Mojgan Zaeimdar, Mahnaz Mirza Ebrahim Tahrani (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Vakilalroaya, M. A., Malmasi, S., Zaeimdar, M., & Mirza Ebrahim Tahrani, M. (2026). Modeling Anthropogenic Urban Fire Risk and Its Environmental Pollution Implications Using LR–WLC Approaches: A Case Study of Kashan, Iran. Anthropogenic Pollution, 10(1). https://doi.org/10.57647/AP.2026.1001.02
Crossref
Scopus
Google Scholar
Europe PMC

PDF views: 42

Abstract

Urban fires represent one of the most significant human-induced hazards in modern cities, often linked to increased pollution emissions and environmental degradation.  This study aims to evaluate fire risk in Kashan using a multi-criteria evaluation (MCE) method based on fuzzy logic, logistic regression, and the analytic hierarchy process (AHP). Initially, criteria and indicators were identified using the Delphi method and normalized using fuzzy logic as capacity and vulnerability factors. The weight of these factors was determined using the AHP method. Subsequently, all layers were combined using the weighted linear combination (WLC) technique. To evaluate the effectiveness of the methods, the outcomes were contrasted with the fire risk map produced via logistic regression. The ROC index was also used to validate the WLC model and logistic regression results. The results reveal that 0.820% of the total studied area (788.96 hectares) and 5% of the area identified by the WLC method (30,483 hectares) are at high risk of urban fires. The ROC index validation results, with values of 0.95 and 0.74 for the logistic regression and WLC methods, respectively, confirmed the superior predictive performance of the LR model. The validated fire risk maps not only support emergency response planning but also highlight spatial correlations between high-risk zones and areas of potential pollutant release or poor air dispersion. These findings demonstrate that mapping human-induced fire risk can provide a valuable spatial framework for air quality management, the mitigation of anthropogenic pollution risk and for advancing sustainable urban environmental management toward cleaner and safer cities.

Keywords

  • Risk Assessment,
  • Fuzzy Logic,
  • Multi-criteria Assessment,
  • Logistic Regression,
  • Anthropogenic Pollution
PDF

References

  1. Aaleagha, M.M., Bozorgmehr, B. and Behbahaninia, A., (2022). Environmental management of forest fire risk using A’SWOT’analysis model (a case study: forest parks in the southern slopes of Alborz, Iran). Anthropog. Pollut. 6(2). Doi: https://doi.org/10.22034/ap.2023.1975447.1141
  2. Ardianto, R., Chhetri, P., Arrowsmith, C., Shahparvari, S. and Shahrom, M., (2025). Modeling residential fire spatial risk using Markov Chain and Geographically Weighted Regression. J. Spat. Sci., pp.1-28. Doi: https://doi.org/10.1080/14498596.2025.2504878
  3. Ayalew, L., Yamagishi, H. and Ugawa, N., (2004). Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides, 1(1), pp.73-81. Doi: https://doi.org/10.1007/s10346-003-0006-9
  4. Bandyopadhyay, J. and Karar, D., (2023). Modeling fire station establishment of industrial area using geo-spatial science. KBES, 4(1), pp.19-36. Doi: https://doi.org/10.51526/kbes.2023.4.1.19-36
  5. Bispo, R., Vieira, F.G., Bachir, N., Espadinha-Cruz, P., Lopes, J.P., Penha, A., Marques, F.J. and Grilo, A., (2023). Spatial modelling and mapping of urban fire occurrence in Portugal. Fire saf. J. 138, p.103802. Doi: https://doi.org/10.1016/j.firesaf.2023.103802
  6. Chhetri, S.K. and Kayastha, P., (2015). Manifestation of an analytic hierarchy process (AHP) model on fire potential zonation mapping in Kathmandu Metropolitan City, Nepal. IJGI, 4(1), pp.400-417. Doi: https://doi.org/10.3390/ijgi4010400
  7. Chisty, M.A. and Rahman, M.M., (2020). Coping capacity assessment of urban fire disaster: An exploratory study on ward no: 30 of Old Dhaka area. IJDRR. 51, p.101878. Doi: https://doi.org/10.1016/j.ijdrr.2020.101878
  8. De Bem, P.P., de Carvalho Júnior, O.A., Matricardi, E.A.T., Guimarães, R.F. and Gomes, R.A.T., (2019). Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil’s Federal District. IJWF. 28(1), pp.35-45. Doi: https://doi.org/10.1071/WF18018
  9. De Smith, M.J., (2004). Distance transforms as a new tool in spatial analysis, urban planning, and GIS. Environ. Plan. B Plan. 31(1), pp.85-104. Doi: https://doi.org/10.1068/b29123
  10. Dey, D., Haque, M.S., Islam, M.M., Aishi, U.I., Shammy, S.S., Mayen, M.S.A., Noor, S.T.A. and Uddin, M.J., (2025). The proper application of logistic regression model in complex survey data: a systematic review. BMC Med. Res. 25(1), p.15. Doi: https://doi.org/10.1186/s12874-024-02454-5
  11. Faramarzi, H., Hosseini, S.M., Pourghasemi, H.R. and Farnaghi, M., (2021). Forest fire spatial modelling using ordered weighted averaging multi-criteria evaluation. J. For. Sci. 67(2), pp.87-100. Doi: https://doi.org/10.17221/50/2020-JFS
  12. Ferreira, T.M., Vicente, R., da Silva, J.A.R.M., Varum, H., Costa, A. and Maio, R., (2016). Urban fire risk: Evaluation and emergency planning. JCH, 20, pp.739-745. Doi: https://doi.org/10.1016/j.culher.2016.01.011
  13. Ghosh, J.K., Bhattacharya, D. and Sharma, S.K., (2012). Fuzzy knowledge based GIS for zonation of landslide susceptibility. Applications of Chaos and Nonlinear Dynamics in Science and Engineering. 2. pp. 21-37. Doi: https://doi.org/10.1007/978-3-642-29329-0_2
  14. Griffiths, S.D., Entwistle, J.A., Kelly, F.J. and Deary, M.E., (2022). Characterising the ground level concentrations of harmful organic and inorganic substances released during major industrial fires, and implications for human health. Environment International, 162, p.107152. Doi: https://doi.org/10.1016/j.envint.2022.107152
  15. Hendry, L., Land, M., Stevenson, M. and Gaalman, G., (2008). Investigating implementation issues for workload control (WLC): a comparative case study analysis. Int. J. Prod. Econ. 112(1), pp.452-469. Doi: https://doi.org/10.1016/j.ijpe.2007.05.012
  16. Horvat, B. and Karleuša, B., (2024). Conceptual Model for Integrated Meso-Scale Fire Risk Assessment in the Coastal Catchments in Croatia. Remote sens. 16(12), p.2118. Doi: https://doi.org/10.3390/rs16122118
  17. Jakhar, R., Samek, L. and Styszko, K., (2023). A comprehensive study of the impact of waste fires on the environment and health. Sustainability, 15(19), p.14241. Doi: https://doi.org/10.3390/su151914241
  18. Jaman, F. and Pantha, F.I., (2023). Fire risk analysis of dhaka south city corporation (dscc) bangladesh. Multidiscip. Sci. J. 1(01), pp.26-39. Doi: https://doi.org/10.4236/cus.2025.133010
  19. Jangjoo, M. R. (2021). Modelling fuzzy multi-criteria decision-making method to locate industrial estates based on geographic information system. Anthropog. Pollut. 5(2). Doi: https://doi.org/10.22034/ap.2021.1933630.1111
  20. Kost, S., Rheinbach, O. and Schaeben, H., (2021). Using logistic regression model selection towards interpretable machine learning in mineral prospectivity modeling. Geochem. 81(4), p.125826. Doi: https://doi.org/10.1016/j.chemer.2021.125826
  21. Kumar, V., Jana, A. and Ramamritham, K., (2022). A decision framework to assess urban fire vulnerability in cities of developing nations: empirical evidence from Mumbai. Geocarto Int. 37(2), pp.543-559. Doi: https://doi.org/10.1080/10106049.2020.1723718
  22. Lee, G., Jun, K.S. and Kang, M., (2019). Framework to prioritize watersheds for diffuse pollution management in the Republic of Korea: application of multi-criteria analysis using the Delphi method. NHESS. 19(12), pp.2767-2779. Doi: https://doi.org/10.5194/nhess-19-2767-2019
  23. Lee, S. and B. Pradhan, (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. JESS.. 115: p. 661-672. Doi: https://doi.org/10.1007/s12040-006-0004-0
  24. Lewis, J.R., (1993). Multipoint scales: Mean and median differences and observed significance levels. Int. J. Hum. Comput. 5(4), pp.383-392. Doi: https://doi.org/10.1080/10447319309526075
  25. Li, G., Zhao, J., Murray, V., Song, C. and Zhang, L., (2019). Gap analysis on open data interconnectivity for disaster risk research. Geo-spat. inf. sci. 22(1), pp.45-58. Doi: https://doi.org/10.1080/10095020.2018.1560056
  26. Lyimo, N.N., Shao, Z., Ally, A.M., Twumasi, N.Y.D., Altan, O. and Sanga, C.A., (2020). A fuzzy logic-based approach for modelling uncertainty in open geospatial data on landfill suitability analysis. ISPRS int. j. geo-inf. 9(12), p.737. Doi: https://doi.org/10.3390/ijgi9120737
  27. Marcoulides, K.M. and Raykov, T., (2019). Evaluation of variance inflation factors in regression models using latent variable modeling methods. Educ. Psychol. Meas., 79(5), pp.874-882. Doi: https://doi.org/10.1177/0013164418817803
  28. Masoumi, Z., van L. Genderen, J. and Maleki, J., (2019). Fire risk assessment in dense urban areas using information fusion techniques. ISPRS int. j. geo-inf. 8(12), p.579. Doi: https://doi.org/10.3390/ijgi8120579
  29. Nebot, A. and Mugica, F., 2021. Forest fire forecasting using fuzzy logic models. Forests, 12(8), p.1005. Doi: https://doi.org/10.3390/f12081005
  30. Muschelli III, J., (2020). ROC and AUC with a binary predictor: a potentially misleading metric. J. classif. 37(3), pp.696-708. Doi: https://doi.org/10.1007/s00357-019-09345-1
  31. Noori, S., Mohammadi, A., Miguel Ferreira, T., Ghaffari Gilandeh, A. and Mirahmadzadeh Ardabili, S.J., (2023). Modelling and mapping urban vulnerability index against potential structural fire-related risks: an integrated GIS-MCDM approach. Fire, 6(3), p.107. Doi: https://doi.org/10.3390/fire6030107
  32. Nyimbili, P.H. and Erden, T., (2020). A hybrid approach integrating entropy-AHP and GIS for suitability assessment of urban emergency facilities. ISPRS int. j. geo-inf. 9(7), p.419. Doi: https://doi.org/10.3390/ijgi9070419
  33. Oloke, O.C., Oluwunmi, O.A., Oyeyemi, K.D., Ayedun, C.A. and Peter, N.J., (2021), February. Fire risk exposure and preparedness of peri-urban neighbourhoods in Ibadan, Oyo State, Nigeria. IOP Conference Series: Earth and Environmental Science, 655(1) p. 012079.Doi: https://doi.org/10.1088/1755-1315/655/1/012079
  34. Pan, J., Wang, W. and Li, J., (2016). Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China. Nat. Hazards Obs. 81(3), pp.1879-1899. Doi: https://doi.org/10.1007/s11069-016-2160-0
  35. Sadollah, A., (2018). Introductory chapter: which membership function is appropriate in fuzzy system? Fuzzy logic based in optimization methods and control systems and its applications, 10. Doi: https://doi.org/10.5772/intechopen.79552
  36. Senaviratna, N.A.M.R. and Cooray, T.M.J.A., (2019). Diagnosing multicollinearity of logistic regression model. Asian J. Probab. Stat., 5(2), pp.1-9. Doi: https://doi.org/10.9734/AJPAS/2019/v5i230132
  37. Schiavo, M., (2025). Quantile-Based approach for improving the identification of preferential groundwater networks. Water, 17(2), p.282. Doi: https://doi.org/10.3390/w17020282
  38. Schneider, L.C. and Pontius Jr, R.G., (2001). Modeling land-use change in the Ipswich watershed, Massachusetts, USA. Agric. Ecosyst. Environ. 85(1-3), pp.83-94. Doi: https://doi.org/10.1016/S0167-8809(01)00189-X
  39. Shariff, N., (2015). Utilizing the Delphi survey approach: A review. J Nurs Care, 4(3), p.246. Doi: https://doi.org/10.4172/2167-1168.1000246
  40. Špatenková, O. and Virrantaus, K., (2013). Discovering spatio-temporal relationships in the distribution of building fires. Fire Saf. J. 62, pp.49-63. Doi: https://doi.org/10.1016/j.firesaf.2013.07.001
  41. Srivanit, M., (2011). Community risk assessment: spatial patterns and GIS-based model for fire risk assessment-a case study of Chiang Mai municipality. JARS. 8(2), pp.113-126. Doi: https://doi.org/10.56261/jars.v8i2.168615
  42. Taridala, S., Yudono, A., Ramli, M.I. and Akil, A., (2017), August. Expert system development for urban fire hazard assessment. Study case: Kendari City, Indonesia. IOP Conference Series: Earth and Environmental Science, 79(1), pp.012-035. Doi: https://doi.org/10.1088/1755-1315/79/1/012035
  43. Thakare, K.V., Tajne, K.M., THAKARE, K.V. and Tajne, K., (2025). A Comprehensive Review of Geographic Information Systems (GIS)-Based Methodologies for Urban Fire Risk Assessment. Cureus J. 2(1). Doi: https://doi.org/10.7759/s44388-024-02916-y
  44. Tikhonova, O., Romão, X. and Salazar, G., (2021). The use of GIS tools for data collection and processing in the context of fire risk assessment in urban cultural heritage. International Conference of Young Professionals «GeoTerrace-2021» 2021(1), pp. 1-5. Doi: https://doi.org/10.3997/2214-4609.20215K3048
  45. Tomar, S., Kaur, A., Dangi, H.K., Ghawana, T. and Sarma, K., (2017). Fire risk analysis using geospatial approach and mitigation measures for South-West Delhi. IJERMT, 6(8), pp.131-137. Doi: https://doi.org/10.23956/ijermt.v6i8.128
  46. Tomaszewski, B., (2020). Geographic information systems (GIS) for disaster management. Routledge. Doi: https://doi.org/10.4324/9781351034869
  47. Wang, Q. and Wang, H., (2022). An integrated approach of logistic-MCE-CA-Markov to predict the land use structure and their micro-spatial characteristics analysis in Wuhan metropolitan area, ESPR, 29(20), pp.30030-30053. Doi: https://doi.org/10.1007/s11356-022-18735-9
  48. Xhafa, S. and Kosovrasti, A., (2015). Geographic information systems (GIS) in urban planning. EJIS. 1(1), pp.74-81. Doi: https://doi.org/10.26417/ejis.v1i1.p85-92
  49. Yalcin, A., Reis, S., Aydinoglu, A.C. and Yomralioglu, T., (2011). A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena, 85(3), pp.274-287. Doi: https://doi.org/10.1016/j.catena.2011.01.014
  50. Zarin, R., Azmat, M., Naqvi, S.R., Saddique, Q. and Ullah, S., (2021). Landfill site selection by integrating fuzzy logic, AHP, and WLC method based on multi-criteria decision analysis. ESPR. 28(16), pp.19726-19741. Doi: https://doi.org/10.1007/s11356-020-11975-7
  51. Zhang, X., Yao, J. and Sila-Nowicka, K., (2018). Exploring spatiotemporal dynamics of urban fires: A case of Nanjing, China. ISPRS int. j. geo-inf. 7(1), p.7. Doi: https://doi.org/10.3390/ijgi7010007
  52. Zhao, Q., Huang, Q., Guo, J. and Zhu, H., (2008). Integrated risk assessment of hazardous chemical installations using GIS and AHP. 4th international conference on wireless communications, networking and mobile computing, pp. 1-5. Doi: https://doi.org/10.1109/WiCom.2008.2472