  This study employed Support Vector Machine (SVM) in the classification and
prediction of fire outbreak based on a fire outbreak dataset captured from the
Fire Outbreak Data Capture Device (FODCD). The fire outbreak data capture
device (FODCD) used was developed to capture the environmental parameters
values used in this work. The FODCD device comprised a DHT11 temperature
sensor, MQ-2 smoke sensor, LM393 Flame sensor, and ESP8266 Wi-Fi module,
connected to Arduino nano v3.0.board. 700 data point was captured using the
FODCD device, with 60% of the dataset used for training while 20% was used for
testing and validation respectively. The SVM model was evaluated using the True
Positive Rate (TPR), False Positive Rate (FPR), Accuracy, Error Rate (ER),
Precision, and Recall performance metrics. The performance results show that
the SVM algorithm can predict cases of fire outbreak with an accuracy of 80%
and a minimal error rate of 0.2%. This system was able to predict cases of fire
outbreak with a higher degree of accuracy. It is indicated that the use of
sensors to capture real-world dataset and machine learning algorithm such as
the support vector machine gives a better result to the problem of fire
management.
