Enhancing Safety and Efficiency in Firefighting Operations via Deep Learning and Temperature Forecasting Modeling in Autonomous Unit

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-05, Vol.23 (10), p.4628
• Main Authors: Ishola, Adenrele A, Valles, Damian
• Format: Article
• Language: English
• Subjects: Accuracy; ANN; ARIMA; Artificial intelligence; autoencoder; Autoregressive models; Carbon monoxide; Casualties; Classification; Construction; Datasets; Deep learning; Deforestation; Embedded systems; Explosions; Fatalities; Fire fighters; Firefighters; firefighting; Fires; Forecasting; Global positioning systems; GPS; High temperature; Injuries; Literature reviews; Machine learning; Model accuracy; Modelling; Neural networks; Prediction models; random forest regression; Sensors; Statistical analysis; Temperature; Traffic flow; Trends; Weather forecasting
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23104628

Firefighters face numerous challenges when entering burning structures to rescue trapped victims, assess the conditions of a residential structure, and extinguish the fire as quickly as possible. These challenges include extreme temperatures, smoke, toxic gases, explosions, and falling objects, which can hinder their efficiency and pose risks to their safety. Accurate information and data about the burning site can help firefighters make informed decisions about their duties and determine when it is safe to enter and evacuate, reducing the likelihood of casualties. This research presents unsupervised deep learning (DL) to classify the danger levels at a burning site and an autoregressive integrated moving average (ARIMA) prediction model to forecast temperature changes using the extrapolation of a random forest regressor. The DL classifier algorithms provide the chief firefighter with an awareness of the danger levels in the burning compartment. The prediction models forecast the rise in temperature from a height ranging from 0.6 m to 2.6 m and the changes in temperature over time at an altitude of 2.6 m. Predicting the temperature at this altitude is critical as the temperature increases faster with height, and elevated temperatures can weaken the building's structural material. We also investigated a new classification method using an unsupervised DL autoencoder artificial neural network (AE-ANN). The prediction data analytical approach included using the autoregressive integrated moving average (ARIMA) with random forest regression implementation. The proposed AE-ANN model, with an accuracy score of 0.869, did not perform as well compared to previous work, with an accuracy of 0.989, at achieving high accuracy scores for the classification task using the same dataset. However, the random forest regressor and our ARIMA models are analyzed and evaluated in this work, while other research has not utilized this dataset, even though it is open-sourced. However, the ARIMA model demonstrated remarkable predictions of the trends of temperature changes in a burning site. The proposed research aims to classify fire sites into dangerous levels and predict temperature progression using deep learning and predictive modeling techniques. This research's main contribution is using a random forest regressor and autoregressive integrated moving average models to predict temperature trends in burning sites. This research demonstrates the potential of using deep learning an