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.
Highlights
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 [1,2,3]
Water jet prediction based on GA-BP neural network 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]
The water jet prediction model based on a GA-BP neural network (BPNN) is constructed and is compared to the water jet prediction model using the traditional BPNN
Summary
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 [1,2,3]. According to the requirements of the BPNN, the hierarchical structure of the network was determined by specifying the number of nodes in the input, hidden, and output layers and selecting the transfer functions. The number of input layer neurons was set to 7 according to the inputs water gun height (h), horizontal angle (α), pitching angle (β), pressure (p), flow (q), wind influence index (windx) and (windy). The number of hidden layer nodes was used to construct the network, and it is determined by the validation data, training and test datasets were used to train and test, the sample error was calculated. The mean square error of the validation data was obtained to predict the neural network performance according to the number of hidden layers nodes. The hidden layer configuration with the lowest network error was observed, and 10 neurons were selected for the hidden layer
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
