zhangzhen

Immersed-Tunnel-Fire-Detection-Data-Set

CFD-Data:

This is a project that encompasses 6 ignition source locations and 32 different smoke exhaust configurations, resulting in a total of 192 FDS computational outcomes. Each individual result is stored in a CSV file, with the file name following a specific pattern: for instance, the filename "100M32" signifies an ignition source located at Y=100 meters, "M" indicating the middle of the ignition lane, "U" denoting one side of the lane, and "32" representing the 32nd smoke exhaust configuration as described in the paper.

ReadDataSet.py:

This is a program that reads CFD-Data and automatically generates a fire source recognition task dataset.

FireLocartionDetection.py:

This is a program designed to identify the location of fire sources and smoke exhaust configurations. It incorporates self-built structures and hyperparameters for BPNN, CNN, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM models, which were determined through grid search to achieve optimal performance. The evaluation metrics include accuracy, precision, recall, and F1 score.

requirements:

numpy==1.19.5 pandas==1.2.4 scikit_learn==0.24.2 tensorflow-gpu==2.5.0