City Fire Risk Assessment Model Based on the Adaptive Genetic Algorithm and BPNetworkJIAO AIHONGDepartment of Fire Commanding Chinese People’s Armed Police Forces Academy Lang fang China, 065000e-mail:ylzmyradio@YUAN LIZHENo.3 Department Nanjing Artillery Academy Langfang China, 065000 e-mail:ylzmyradio@Abstract—Based on the risk evaluation index system of city fire, a comprehensi ve evaluati on model wi th the adapti ve geneti c algorithm and BP neural network (AGA-BP) is established in the arti cle.In former process of the hybri d algori thm, the adapti ve geneti c algori thm i s appli ed to adjust wei ghts and thresholds of the three-layer BP neural network and train the BP neural network for locati ng the global opti mum, and the error back propagat i on algor i thm i s used to search i n ne ghborhoods of the approx mate opt mal solut on n the later process. The program wri tten i n VB6.0 i s used to learn some samples of c i ty f i re r i sk accord i ng to the AGA-BP algorithm and the general BP algorithm. The results show that the learning precision of AGA-BP algorithm is more correctly than that of the general BP algorithm. The training speed and convergence rate of the former i s s i gn i f i cantly i mproved because of the combi nati on of AGA and BP algori thm. It i s helpful to realize automated evaluation for city fire risk.Keywords-fire risk assessment; adaptive genetic algorithm; back propagation algorithmI.I NTRODUCTIONCity fire risk assessment is given a comprehensive evaluation conclusion on the probability of fire accidents and the vulnerability assessment of city facilities and the resistance ability of fire in the city,which is based on statistical analysis of city history fire data and hazard identification of the heavy danger sources. At present, the research on city fire risk assessment work is still very weak. Some foreign scholars are mainly concerationed on how to assess the city fire risk and reduce city fire losses and giving some assessment methods. It is helpful to plan city fire force and give a fire safety grade to the district by the fire risk evaluation conclusion. The home researchers is mostly focused on giving a synthetic evaluation conslusion for a certain producing enterprise or a particular building, while for fire risk assessment of the whole city is at a early stage presently.With the development of economy, there are more and more large and high buildings in big cities,and the spatial morphology is changing, and the population is increasing, and the wealth concentrated increasingly, oil, gas, electricity and decoration materials are widespread used in our living life, so the structure of city is complex, and the number of city fire hazards is growing.The safety evaluation methods in common use is including safety check list method, accident type and analysis method, fuzzy synthetic evaluation method, accident tree method, analytic hierarchy process and so on. These methods are short of further studies about the effect factors of fire, because the city security against fire as a whole, density of population, quantity of electricity and other factors are fireare interrelated, interaction and mutual checks each other. So, we need to notice that the evaluation process is dynamic and nonlinear. If we use artificial neural networks (ANN) and expert system to simulate the judgement reasoning and the decision-making process of city fire risk evaluation process, the limitations of traditional methods and the subjectiveness of experts can be avoided because of its good evaluation model structure and working platform.II.E RROR B ACK PROPAGATION ALGORITHMFigure 1three-layer BP network structure. TheThe three-layer BP neural network structure is shown in Fig.1. Error back propagation algorithm is one of the most popular neural network learning algorithms,which has been used widely in many fields, such as pattern recognition, fault diagnosis and automatic controls[1]. The BP algorithm trains a given feed-forward multilayer neural network for a given set of input patterns with known samples. When each entry of the sample set is presented to the network, the network examines its output response to the sample input pattern. The output response is then compared to the known and desired output and the error value is calculated. Based2012 International Conference on Industrial Control and Electronics Engineeringon the error, the connection weights and thresholds are adjusted.The backpropagation algorithm is based on Widrow-Hoff delta learning rule in which the weight adjustment is done through mean square error of the output response to the sample input. The set of these sample patterns are repeatedly presented to the network until the error value is minimized.III.A DAPTIVE G ENETIC A LOGRITHMSimple Genetic Algorithms (SGA) was firstly proposed by John Holland and his students[2].Now,genetic algorithms have been extensively used in different domains as a type of robust optimization method. However, genetic algorithm todemonstrate the more serious question is “premature convergence” problem, less capable local optimization, the later slow convergence and can not guarantee convergence to the global optimal solution and so on. In recent years,many scholars try to improve genetic algorithms, such asimproving the encoding scheme, fitness function, geneticoperator design. For this reason, the adaptive genetic algorithms is proposed in this paper, which the crossoveroperation c and mutation operation at randomrespectively as fellow: p m p 1211'''()()c c cc max c p p f f p f p f f p f ­ !°° ®°d °¯f f (1) 1211()()m m m m max m p p f f p f p f fp f ­ !°° ®°d °¯f f(2)Here, max fis the best individual’s fitness, 'f is the better individual’s fitness in every group, f is the average fitness, and f is every individual’s fitness in the currentgeneration. IV.F IRE RISK E VALUATION MODEL BASED ON AGA ̢BP AL GORITHMIn actually, the city fire is a complex system, which is not only having a great number of risk evaluation indexes but also having a large calculation about the indexes and a wide area involved. So, a number of clear concepts and clear borderlines and readily accepted indexes are summarized in accordance with its intrinsic link and affiliation to create the evaluation indexes system. We can use the system to evaluate the fire condition of a city by the quantitative analysis and qualitative analysis method and get a comprehensive assessment score. To ensure some contexts of the system, it is need for extensive research and further detailed analysis and synthesis.Based on absorbing the anterior research fruit of other people and city fire fundamental characteristics, some evaluation indicatorsincluding the historical circumstances of city fire 1x , the development of city economic 2x , the characteristics of city industry 3x , the characteristics of city buildings 4x ,the layout of city structure 5x , the construction of municipal facilities 6x , the construction of fire control forces 7x , the masses fire qualities 8x , the fire safety culture 9x and the city meteorological conditions 10x is created in the paper. To choose the best connection weights and thresholds for a three-layer BP neural network, a combination algorithm with adaptive genetic algorithm and error back propagation algorithm is put forward for the city fire evaluation. Here are some evaluation steps about how to get the city fire comprehensive assessment score based on the AGA-BP algorithm [3].Step 1. Set a fire risk assessment objects set as learning samples. Step 2. Create a risk evaluation indexes system.It can portrait the risk condition from different aspects.Step 3. Initialize the connection weights and thresholds in the three-layer BP network for the risk evaluation indexesby generating some random numbers Some chromosomes are constructed with real-code schema in [] like X .k k a b ˈ11111111111111(,)i n j ij nj j p ip np p j p t jt pt t q jq pq q X w w w w w w w v v v v v v v v v T T T J J J """"""""""""""" ˈˈ w w (3)Some GA parameters including the population size popsize , the crossover probability ,, the mutation probability ,, the max evolutional generation MaxGen and the 1c P 2c P 1m P 2m P variable evolutional generation Gen are initialized, and BP parameters including iteration times epoch , learning rate D ,E are also initialized. If the error function can be represented as2111(2q m kk t t k t E y ¦¦)c (4)Then the fitness function f is defined as1/f E (5) Step 4.Set the variable number Gen asGen Gen (6) Step 5.Choose some samples from the fire risk assessment objects set by turn to the BP neural network, and calculate the mean-square error according to the actualoutput and the expected output, then get the fitness of chromosome X from (5).Step 6. Make some genetic operations in the evolution process. Selection is adopted by roulette wheel and keeping the best individual of each generation, crossover and mutation with randomly as (1), (2) by the fitness of every individual to get new connection weights and thresholds.Step 7. If the evolution process is completed, output the optimal individual, then go to step 8, or else, go back to step 4.Step 8.Do some times iteration calculation using BP algorithm for the optimal individual in the evolution process, then get the near global optimal solution of the problem.Step 9. Check the correctness and validity of the BP neural network, if the learning precision is satisfied, we can use it to solve some similar problems.V.C ON CLUSIONChoose the data in table ĉ as learning samples to the three–layer 10×25×1 BP neural network, table Ċ is the result of comparing with the AGA-BP algorithm and the traditional BP algorithm, which are written by VB6.0 with the start of same connection weights ,{}ij w {}jt v and thresholds {}j T ,{}t J . Here are The AGA-BP algorithm parameters:Popsize=50, MaxGen=2000, epoch =2000, =0.9, =0.6, 1c P 2c P 1m P =0.01 and 2m P =0.001.In the AGA-BP algorithm, The BP neural network parameters epoch =25000. By monitoring the convergence of error values during the learning process, some conclusions can be drawn as follow:(1) To get the result in table Ċ, it spends the AGA-BP Algorithm 76.203 seconds and the BP algorithm 90.578 seconds in iterative computing in iterative computing in the PC with Intel ® Core™2 Duo Processor T5500 and 2GB memory and Windows XP operation system. It indicatesthat genetic algorithm can shorten training time of the BP neural network, and it can get a smaller error because of genetic algorithm in the AGA-BP algorithm. After the learning process is completed, the former error is nearby 0.00006 and the later is 0.00078.(2)To get the global error (˘0.001, the former algorithm reachs the convergence value in the 987th iterative calculation, while the later algorithm gets it in the 22033 th iterative calculation. It’s also shown that the AGA-BP algorithm is powerful,which combines the advantages of genetic algorithm with parallel computing and strong global searching capacity and the advantages of BP algorithm with powerful local-optimization ability.It is helpful to realize automated evaluation for city fire risk.TABLE II. T HE L EARNING R ESULT OF T HE S AMPLES AGA-BP algorithmBP algorithmNoExcepted OutputOutputRelative Error (%)OutputRelative Error (%)10.9330.93082-0.2335570.941370.89699920.856 0.86954 1.5816890.83849-2.04601230.792 0.793940.2449360.77832-1.72742140.863 0.86087-0.2463680.86049-0.29123050.688 0.68395-0.5882410.67957-1.22567860.852 0.852050.0006250.83610-1.86614370.739 0.739250.0334160.75828 2.60829180.801 0.802820.2266540.81095 1.24194990.682 0.682640.094440.684700.395739100.6350.639280.6746870.63184-0.497592R EFERENCES[1]Zhongzhi Shi, Neural Computation, Beijing:Press of electronic Industry, 1993.[2]J.H. Holland, Outline for a logical theory of adaptive systems, J. Assoc. Computer, Mach. 3 (1962) 297–314.[3]Aihong, Jiao and Lizhe, Yuan. Fault diagnosis based on adaptive genetic algorithm and BP neural network:ICCET 201 ü2010 International Conference on Computer Engineering and Technology, Proceedings, v6, p 427-430, 16-18 Apr 2010 [C], chengdu,China.TABLE I. T HE S AMPLESEvaluation Elements No 1x 2x 3x 4x 5x 6x 7x 8x 9x 10x Result11x 10.880.920.880.970.950.890.910.930.970.920.93320.810.850.790.830.800.860.840.810.850.840.85630.780.810.740.750.770.790.730.760.720.780.79240.840.830.880.820.760.880.850.870.830.810.86350.660.740.710.700.660.680.670.730.750.720.68860.840.770.910.840.810.860.830.790.870.840.85270.720.710.730.750.760.790.720.750.760.740.73980.870.820.850.870.820.830.760.770.790.780.80190.740.700.630.680.640.720.710.640.690.670.682100.610.600.620.660.610.600.640.670.650.620.635。