Fire prediction in urban villages based on improved grey wolf optimized BP network

doi:10.16265/j.cnki.issn1003-3033.2025.08.1426

Abstract

Abstract:

In order to prevent fires in urban villages, IGWO and BP neural network were used to predict the risk of fires in urban villages. By introducing nonlinear convergence factors and mutation operators, the traditional grey wolf optimizer (GWO) was improved to enhance its global search capability, convergence speed, and stability. Furthermore, a fire risk prediction model for urban villages based on IGWO optimized BP neural network (IGWO-BP) was constructed. Taking into account the complexity and specificity of urban village fire risk factors, an indicator system was developed to predict fire risk, and an empirical study was conducted for verification. The results show that IGWO has significantly improved global search ability, convergence speed, and stability compared to traditional GWO, particle swarm optimization (PSO), and the Great Wall construction algorithm (GWCA). The IGWO-BP model can predict fire risk in urban villages by processing fire risk indicators.

Key words: improved grey wolf optimizer (IGWO), back propagation (BP) neural network, urban villages fire, risk prediction, mutation operator, high-dimensional function

CLC Number:

X913.4安全系统工程

LYU Shuran, TIAN Jiangxue, DANG Xinyu. Fire prediction in urban villages based on improved grey wolf optimized BP network[J]. China Safety Science Journal, 2025, 35(8): 196-204.

Figures/Tables 11

Table 1

Table 2

Fig.1

Table 3

Fig.2

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Table 5

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函数	表达式	搜索空间
F₁	${f}_{1}\left(x\right)=\stackrel{\mathrm{n}}{\sum _{i=1}}{x}_{i}^{2}$	[-100,100]
F₂	${f}_{2}\left(x\right)=\left.\stackrel{\mathrm{n}}{\sum _{i=1}}\right\|{x}_{i}\|+\left.\stackrel{\mathrm{n}}{\prod _{i=1}}\right\|{x}_{i}\|$	[-10,10]
F₃	${f}_{3}\left(x\right)=\stackrel{\mathrm{n}}{\sum _{i=1}}{\left(\stackrel{\mathrm{i}}{\sum _{j=1}}{x}_{j}\right)}^{2}$	[-100,100]
F₄	${f}_{4}\left(x\right)=\underset{\mathrm{i}}{\mathrm{m}\mathrm{a}\mathrm{x}}\left\{\right\|{x}_{i}\|,1\le i\le n\}$	[-100,100]
F₅	${f}_{5}\left(x\right)=\stackrel{\mathrm{n}}{\sum _{i=1}}i{x}_{i}^{4}+\mathrm{r}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{o}\mathrm{m}\left[\mathrm{0,1}\right)$	[-1.28,1.28]
F₆	${f}_{6}\left(x\right)=\stackrel{\mathrm{n}}{\sum _{i=1}}[{x}_{i}^{2}-10\mathrm{c}\mathrm{o}\mathrm{s}(2\mathrm{\pi }{x}_{i}\left)\right]+10n$	[-5.12,5.12]
F₇	${f}_{7}\left(x\right)=-20\mathrm{e}\mathrm{x}\mathrm{p}\left(-0.2\sqrt{\frac{1}{\mathrm{n}}\stackrel{\mathrm{n}}{\sum _{\mathrm{i}=1}}{\mathrm{x}}_{\mathrm{i}}^{2}}\right)-\mathrm{e}\mathrm{x}\mathrm{p}\left(\frac{1}{\mathrm{n}}\stackrel{\mathrm{n}}{\sum _{\mathrm{i}=1}}\mathrm{c}\mathrm{o}\mathrm{s}2\mathrm{\pi }{\mathrm{x}}_{\mathrm{i}}\right)+20+\mathrm{e}$	[-32,32]
F₈	${f}_{8}\left(x\right)=\frac{1}{4\mathrm{ }000}\stackrel{\mathrm{n}}{\sum _{i=1}}{x}_{i}^{2}-\stackrel{\mathrm{n}}{\prod _{i=1}}\mathrm{c}\mathrm{o}\mathrm{s}\left(\frac{{\mathrm{x}}_{\mathrm{i}}}{\sqrt{\mathrm{i}}}\right)+1$	[-600,600]

函数	GWO		IGWO		PSO		GWCA
函数	均值	标准差	均值	标准差	均值	标准差	均值	标准差
F₁	2.50×10^-27	7.79×10^-27	3.39×10^-205	1.04×10^-107	2.56×10⁰	1.06×10⁰	1.35×10^-1	7.00×10^-1
F₂	1.08×10^-16	7.31×10^-17	4.93×10^-107	4.15×10^-107	4.31×10⁰	1.18×10⁰	4.01×10^-1	7.72×10^-1
F₃	1.15×10^-5	3.08×10^-5	1.76×10^-167	5.98×10^-95	1.82×10²	5.41×10¹	2.77×10³	1.91×10³
F₄	8.77×10^-7	8.92×10^-7	1.12×10^-93	2.21×10^-93	2.02×10⁰	2.39×10^-1	2.83×10¹	4.20
F₅	2.49×10^-3	1.12×10^-3	1.00×10^-4	9.74×10^-5	1.47×10¹	9.67×10⁰	1.59×10⁰	6.55×10^-1
F₆	2.79×10⁰	4.22×10⁰	0	0	1.72×10²	2.76×10¹	5.75×10¹	1.24×10¹
F₇	1.03×10^-13	2.16×10^-14	4.00×10^-15	0	2.73×10⁰	4.67×10^-1	1.10×10¹	1.26×10⁰
F₈	4.67×10^-3	7.67×10^-3	0	0	1.35×10^-1	4.85×10^-2	1.30×10⁰	1.48×10⁰

函数	维数	GWO		IGWO		PSO		GWCA
函数	维数	均值	标准差	均值	标准差	均值	标准差	均值	标准差
F₁	500	1.88×10^-3	5.10×10^-4	1.58×10^-164	0	1.25×10⁴	8.12×10²	1.66×10⁵	2.78×10⁴
F₁	1 000	2.81×10^-1	7.71×10^-2	3.08×10^-161	7.20×10^-161	7.32×10⁴	2.33×10³	5.04×10⁵	7.18×10⁴
F₂	500	1.03×10^-2	1.42×10^-3	5.18×10^-85	1.50×10^-85	2.15×10²	2.40×10¹⁵	8.23×10²	1.17×10²
F₂	1 000	6.23×10^-1	3.58×10^-1	1.00×10^-83	9.81×10^-84	1.69×10³	1.31×10²⁵	1.50×10³	2.61×10²
F₃	500	3.62×10⁵	8.23×10⁴	2.61×10^-141	3.48×10^-139	5.60×10⁵	1.30×10⁵	1.46×10⁶	6.43×10⁵
F₃	1 000	1.16×10⁶	2.26×10⁵	8.63×10^-139	7.73×10^-138	3.07×10⁶	6.81×10⁵	7.55×10⁶	2.64×10⁶
F₄	500	6.73×10¹	5.37×10⁰	2.12×10^-60	2.88×10^-54	3.02×10¹	1.62×10⁰	5.61×10¹	1.70×10⁰
F₄	1 000	7.63×10¹	4.08×10⁰	4.10×10^-33	3.87×10^-10	3.52×10¹	1.55×10⁰	6.04×10¹	1.94×10⁰
F₅	500	2.75×10^-2	1.27×10^-2	3.59×10^-5	9.87×10^-5	5.54×10⁴	1.92×10³	1.75×10³	5.15×10²
F₅	1 000	2.22×10^-1	3.77×10^-2	1.62×10^-4	1.15×10^-4	2.41×10⁵	6.93×10³	1.33×10⁴	2.25×10³
F₆	500	6.99×10¹	2.16×10¹	0	0	8.13×10³	2.01×10²	3.95×10³	1.50×10²
F₆	1 000	1.83×10²	3.86×10¹	0	0	1.67×10⁴	2.35×10²	8.91×10³	2.97×10²
F₇	500	1.79×10^-3	3.60×10^-4	1.02×10^-16	0	1.41×10¹	2.83×10^-1	1.66×10¹	4.10×10^-1
F₇	1 000	1.55×10^-2	2.42×10^-3	4.00×10^-15	0	1.71×10¹	1.91×10^-1	1.70×10¹	3.46×10^-1
F₈	500	1.40×10^-4	2.88×10^-2	0	0	1.92×10¹	2.12×10⁰	1.51×10³	2.15×10²
F₈	1 000	2.19×10^-1	6.74×10^-2	0	0	1.24×10²	7.71×10⁰	5.15×10³	5.42×10²

一级指标	二级指标
环境因素	建筑老旧程度E₁
	建筑材料易燃程度E₂
	电线老化程度E₃
	电器使用情况E₄
	建筑用途复杂程度E₅
	燃气使用率E₆
	易燃物储存情况E₇
	煤气-燃气管道及电气线路铺设状况E₈
	管道老化情况E₉
人口因素	人口密度E₁₀
	外来人口比例E₁₁
	老年人儿童比例E₁₂
	人员安全意识E₁₃
管理因素	消防安全教育及培训E₁₄
管理因素	火灾风险评估及隐患排查E₁₅
其他因素	区域气候特征(温度、湿度)E₁₆
其他因素	雷击、纵火、燃放爆竹等因素E₁₇

编号	火灾起数	面积/km²	概率/(起·km^-2)
1	2	2.84	0.704
2	2	3.07	0.651
3	1	1.41	0.711
4	2	3.23	0.620
5	3	4.06	0.740
6	2	3.04	0.657
7	1	1.38	0.725
8	2	2.77	0.723
9	2	3.27	0.612
10	2	2.86	0.699
11	2	2.90	0.689
12	2	3.20	0.625
13	1	1.49	0.669
14	1	1.26	0.795
15	1	1.65	0.606
16	2	2.54	0.789
17	1	1.35	0.739
18	1	1.54	0.648
19	1	1.30	0.767
20	2	2.68	0.748
21	2	2.94	0.680
22	1	1.60	0.627
23	2	2.50	0.800
24	3	4.06	0.738
25	2	2.57	0.778
26	1	1.62	0.619
27	1	1.53	0.653
︙	︙	︙	︙
360	2	3.35	0.597