Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Fire Temperature Prediction Based on LSTM
With the acceleration of urbanization and the increase in population density, the frequency and scale of fires have been expanding, and fire has become one of the most important factors threatening the safety of human life and property. Fire temperature is one of the important parameters for assessing fire danger and developing fire suppression strategies. Accurate prediction of fire temperature is important for fire prevention and emergency response. In this paper, we took fire temperature as the research object and used LSTM model to analyze fire scenarios. We established a fire temperature prediction model to predict the fire temperature by combining other features and environmental factors, which improved the prediction accuracy of fire temperature. The experimental results on real fire scenario datasets have shown that the fire temperature prediction model using LSTM network had higher accuracy than traditional prediction models. It can provide more accurate references for disaster prevention and mitigation work, reducing the losses caused by fires.
Fire Temperature Prediction Based on LSTM
With the acceleration of urbanization and the increase in population density, the frequency and scale of fires have been expanding, and fire has become one of the most important factors threatening the safety of human life and property. Fire temperature is one of the important parameters for assessing fire danger and developing fire suppression strategies. Accurate prediction of fire temperature is important for fire prevention and emergency response. In this paper, we took fire temperature as the research object and used LSTM model to analyze fire scenarios. We established a fire temperature prediction model to predict the fire temperature by combining other features and environmental factors, which improved the prediction accuracy of fire temperature. The experimental results on real fire scenario datasets have shown that the fire temperature prediction model using LSTM network had higher accuracy than traditional prediction models. It can provide more accurate references for disaster prevention and mitigation work, reducing the losses caused by fires.
Fire Temperature Prediction Based on LSTM
Hong, Yonglin (Autor:in) / Xue, Tianyu (Autor:in) / Huang, Muyang (Autor:in)
28.06.2024
2135978 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch