Research Article: Prediction model for the water jet falling point in fire extinguishing based on a GA-BP neural network

Date Published: September 4, 2019

Publisher: Public Library of Science

Author(s): ChaoYi Zhang, Ruirui Zhang, ZhiHui Dai, BingYang He, Yan Yao, Jie Zhang.

http://doi.org/10.1371/journal.pone.0221729

Abstract

Past research on the process of extinguishing a fire typically used a traditional linear water jet falling point model and the results ignored external factors, such as environmental conditions and the status of the fire engine, even though the water jet falling point location prediction was often associated with these parameters and showed a nonlinear relationship. This paper constructed a BP (Back Propagation) neural network model. The fire gun nozzle characteristics were included as model inputs, and the water discharge point coordinates were the model outputs; thus, the model could precisely predict the water discharge point with small error and high precision to determine an accurate firing position and allow for the timely adjustment of the spray gun. To improve the slow convergence and local optimality problems of the BP neural network (BPNN), this paper further used a genetic algorithm to optimize the BPNN (GA-BPNN). The BPNN can be used to optimize the weights in the network to train them for global optimization. A genetic algorithm was introduced into the neural network approach, and the water jet landing prediction model was further improved. The simulation results showed that the prediction accuracy of the GA-BP model was better than that of the BPNN alone. The established model can accurately predict the location of the water jet, making the prediction results more useful for firefighters.

Partial Text

An analysis of the trajectory and state of the water jet is a key step in analysing the accuracy of fire extinguishing; the determination of the firing point location provides the basis for the adjustment of the relevant equipment parameters [1–3]. The location of the ignition point is the basis of water jet control, so intelligent fire-fighting equipment is the synergistic combination of ignition source location detection and jet flow control [4]. Most of the water jet control methods are based on the relationship between the ignition point and the angle of the water gun, but this approach cannot determine whether the water jet accurately falls at the ignition point [5]. The ultimate goal of firefighting water gun control is to accurately hit the ignition source point, in which case fire detection and the calculation of the ignition point space coordinates are adequate for a water sprinkler fire in the early stage of development [6]; the basic water jet state and its control situation can affect fire extinguishing, so research on the water jet is a key focus for fixed-point fire extinguishing [7].

This section covers the verification of the simulation and the analysis of the abovementioned algorithm. The water jet prediction model based on a GA-BPNN is constructed and is compared to the water jet prediction model using the traditional BPNN. The parameter setting part of the GA-BPNN was consistent with that of the BP network in the previous section. In addition, the GA-BPNN genetic algorithm parameter settings are shown in Table 5.

This paper used the classical BPNN to model and predict the water jet falling point of a fire gun and designed a network structure suitable for the fire water jet landing point model, including the data collection, data preprocessing, network weight setting, transfer function selection and other parameter settings. Then, experiments and predictions were carried out. As a result, it was found that the prediction error of the water jet landing point based on the BPNN was relatively large. Then, a water jet prediction model based on the GA-BPNN was proposed. The article adopted the optimization structure of the genetic algorithm to analyse and optimize the neural network weights and thresholds, and, to a large extent, address the problems of the BPNN training process in terms of local minima and other issues. The simulation results showed that the accuracy of the GA-BP model prediction was better than that of the BPNN. The established model can accurately predict the location of the water jet, making the prediction result more useful for firefighters.

 

Source:

http://doi.org/10.1371/journal.pone.0221729