Fire risk zoning in urban areas using logistic regression method (Case study: Kashan city)

Subject Areas : Spatial data infrastructures and standardisation

Mohammad Amin Vakilalroaya ¹ , Saeed Malmasi ² , Mojgan Zayeem Dar ³ , Mahnaz Mirza Ebrahime Tehrani ⁴

1 - PhD Student in Environment - Environmental Planning, Faculty of Marine Science and Technology, Islamic Azad University of North Tehran Branch, Tehran, Iran
2 - Assistant Professor, Department of Environmental Science, Faculty of Marine Science and Technology, Islamic Azad University of North Tehran Branch, Tehran, Iran
3 - Assistant Professor, Department of Environmental Science, Faculty of Marine Science and Technology, Islamic Azad University of North Tehran Branch, Tehran, Iran
4 - Assistant Professor, Department of Environmental Science, Faculty of Marine Science and Technology, Islamic Azad University of North Tehran Branch, Tehran, Iran

Received: 2021-07-17 Accepted : 2021-10-04 Published : 2022-10-23

Keywords: Logistic regression, Validation, Fire risk zoning, spatial relationship analysis, Fuzzy,

Abstract :

Background and Objective Fire risk management is a global issue, and urban safety policies must take it seriously. One of the fields of research for controlling urban fires is to identify critical fire points in the region; Insufficient knowledge of these points will cause the occurrence and spread of fire in various areas and uses, delays in controlling it, and causing financial losses and personal injury as well as environmental pollution. Fire risk zoning with the aim of being used in planning and management in urban fire control has not been considered in the study area of this research and no research has been done in this field in the form of research and study plan. The purpose of this study is to determine and identify the criteria for fire risk zoning in the study area, to create a fire risk map based on the logistic regression method and compliance with the fire reality map, and also to present fire management programs and fire crisis management in Kashan.Materials and Methods The steps and techniques used in this study were performed in six steps. The first step is to identify the effective criteria and indicators. Using library studies, information obtained from authoritative articles, and also through the Delphi method in order to collect the opinions of experts, the Likert scale was used. In the second step, the screening of criteria was done in accordance with the purpose. The effective criteria in this study are vulnerable factors including (population density, industrial units, commercial-warehouse, high-rise buildings, old tissue, and fuel station) as well as the capacity of reducing factors. (Fire station, roads, and hydrant valves). In the next step, the layers were standardized using fuzzy logic. At first, the distance function was performed on the criteria in SELVA IDRISI to determine the distance from each phenomenon. Then, by the fuzzy method, all the criteria determined in the range of zero to 255 were standardized. The type of function used in the fuzzy logic approach is linear, and the selection of the function type and thresholds was based on a review of sources and expert opinion. In order to analyze the spatial relationship between fire incidents that occurred in the city and the role of effective factors in its occurrence, all fire points of the last 10 years in the city from 2010 to 2020 were extracted and turned into a raster map. In the fifth step, a Fire hazard map was prepared using logistic regression. After determining the validity of the logistic regression model using the specified indicators, the fire risk zoning map of Kashan was drawn. In the last step, Chi-Square, ROC, and Pseudo R Square were used to validate the logistic regression model.Results and Discussion Advantages of using the logistic regression model In addition to modeling observations, it is possible to predict the probability of each person belonging to each of the levels of dependent variables and the possibility of directly calculating the odds of variables using the maximum likelihood of maximum model coefficients. Also, compared to other statistical techniques, multivariate such as multiple regression analysis and diagnostic analysis, the dependent variable can have only two variables, one is the probability of an accident and the other is its non-occurrence. In order to analyze the spatial relationship between fire incidents that occurred in the city and the role of effective factors in its occurrence, all fire points of the last 10 years in the city from 2010 to 2020 were extracted and turned into a raster map. The output of the logistic regression model has coefficients between 0 and 1, with a probability higher than 0.5 of a value of one (occurrence of fire) and a probability of less than 0.5 of a value of zero (no occurrence of fire) and thus a boolean map of risk is generated. This logarithmic change causes the predicted probability to be continuous in the range of 0 to 1, and the output of the model to be presented as a spatial prediction map of the probability of destruction. Then, in the logistic regression equation, this layer was introduced as a dependent variable and the effective parameters in fire zoning were introduced as an independent variable. After entering the data into the logistic regression statistical model, model coefficients were extracted using effective parameters in IDRISI software. After determining the validity of the logistic regression model using the specified indicators, a fire risk zoning map of Kashan was drawn. Finally, in terms of fire risk potential, the study area was divided into 5 classes: very low, low risk, medium, very high, and high risk. The area of each of the 5 classes was obtained in hectares and the percentages were 87475.47, 4669.03, 132115, 1116.33, 788.96 hectares, and 90.94, 4.85, 2.19, 1.16, 0.82 percent, respectively.Conclusion The value of 0.95 obtained from ROC indicates a very high correlation between the independent and dependent variables. The value of the qi index is twice equal to 110836.07; Since its value is much higher than the threshold value, then the null hypothesis of all coefficients is also rejected. The value of the PR2 test in this study was 0.47, so the logistic regression model had an acceptable fit.

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