{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T16:42:18Z","timestamp":1792168938199,"version":"4.4.0"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T00:00:00Z","timestamp":1683676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Dell EMC","award":["4202981000"],"award-info":[{"award-number":["4202981000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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\u2019s 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 and predictive modeling to enhance firefighter safety and decision-making processes.<\/jats:p>","DOI":"10.3390\/s23104628","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T01:37:24Z","timestamp":1683769044000},"page":"4628","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Enhancing Safety and Efficiency in Firefighting Operations via Deep Learning and Temperature Forecasting Modeling in Autonomous Unit"],"prefix":"10.3390","volume":"23","author":[{"given":"Adenrele A.","family":"Ishola","sequence":"first","affiliation":[{"name":"Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5684-9498","authenticated-orcid":false,"given":"Damian","family":"Valles","sequence":"additional","affiliation":[{"name":"Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"ref_1","unstructured":"National Fire Protection Association (2020, October 10). \u201cUnited States Firefighter Injury Report 2018\u201d. NFPA Fire Analysis and Research, Quincy, MA, USA, April 2020. Available online: https:\/\/www.nfpa.org\/-\/media\/Files\/News-and-Research\/Fire-statistics-and-reports\/Emergency-responders\/FirefighterInjuries2019.ashx."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Khana, N., Leea, D., Alia, A.K., and Parka, C. (2020, January 27\u201328). Artificial intelligence and blockchain-based inspection data recording system for portable firefighting equipment. Proceedings of the International Symposium on Automation and Robotics in Construction, Kitakyushu, Japan.","DOI":"10.22260\/ISARC2020\/0130"},{"key":"ref_3","unstructured":"National Fire Protection Association (2020, September 20). Firefighter Deaths by Cause and Nature of Injury. Available online: http:\/\/www.nfpa.org\/News-and-Research\/Data-research-and-tools\/Emergency-Responders\/Firefighter-fatalities-in-the-United-States\/Firefighter-deaths-by-cause-and-nature-of-injury."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bhattarai, M., Jensen-Curtis, A.R., and Mart\u00ednez-Ram\u00f3n, M. (2020, January 14\u201317). An embedded deep learning system for augmented reality in firefighting applications. Proceedings of the 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA.","DOI":"10.1109\/ICMLA51294.2020.00193"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, J. (2009, January 1\u20133). Case-based reasoning intelligent decision approach for firefighting tactics. Proceedings of the 2009 Second International Conference on Intelligent Networks and Intelligent Systems, Tianjian, China.","DOI":"10.1109\/ICINIS.2009.118"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pinales, A., and Valles, D. (2018, January 1\u20133). Autonomous embedded system vehicle design on environmental, mapping and human detection data acquisition for firefighting situations. Proceedings of the 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.","DOI":"10.1109\/IEMCON.2018.8615022"},{"key":"ref_7","unstructured":"(2022, September 23). Carbon Monoxide Poisoning and Firefighters. Available online: https:\/\/www.iaff.org\/carbon-monoxide\/."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tian, Y., Wei, C., and Xu, D. (2020, January 20\u201322). Traffic flow prediction based on stack autoencoder and long short-term memory network. Proceedings of the 2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), Shenyang, China.","DOI":"10.1109\/AUTEEE50969.2020.9315723"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/TPWRS.2016.2628873","article-title":"Short-term electricity price forecasting with stacked denoising autoencoders","volume":"32","author":"Wang","year":"2016","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Zhu, P., Huang, Z., and Zhao, Q. (2020, January 13\u201315). Pest Bird Density Forecast of Transmission Lines by Random Forest Regression Model and Line Transect Method. Proceedings of the 2020 7th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS), Guangzhou, China.","DOI":"10.1109\/ICCSS52145.2020.9336898"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"He, Y., Li, J., Bi, F., Mao, Y., Li, B., Chen, C., and Chu, Y. (2022, January 22\u201324). Prediction of Near-surface Air Temperature at Glacier Region using ERA5 Data based on Random Forest Regression method. Proceedings of the 2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS), Zhoushan, China.","DOI":"10.1109\/ICGMRS55602.2022.9849373"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"124204","DOI":"10.1109\/ACCESS.2020.3004519","article-title":"Prediction of the electrical strength and boiling temperature of the substitutes for greenhouse gas SF\u2086 using neural network and random forest","volume":"8","author":"Sun","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, C., Chen, Z., Cui, J., Wang, Y., and Zou, F. (2014, January 24\u201327). The lithium-ion battery state-of-charge estimation using random forest regression. Proceedings of the 2014 Prognostics and System Health Management Conference (PHM-2014 Hunan), Zhangjiajie, China.","DOI":"10.1109\/PHM.2014.6988190"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chatwaranon, Y., and Tunyasrirut, S. (2022, January 24\u201327). Weather Forecast Comparison between ARIMA and Exponential Smoothing in the Cloud. Proceedings of the 2022 19th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Prachuap Khiri Khan, Thailand.","DOI":"10.1109\/ECTI-CON54298.2022.9795590"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, Q. (2021, January 24\u201326). Time series simulation method of meteorological elements based on ARIMA model. Proceedings of the 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), Zhuhai, China.","DOI":"10.1109\/ICBASE53849.2021.00073"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shamsnia, S.A., Babazadeh, H., and Boustani, F. (2010, January 10\u201312). Using stochastic methods in modeling of the monthly precipitation and mean temperature (A case study: Shiraz station, Iran). Proceedings of the 2010 International Conference on Environmental Engineering and Applications, Singapore.","DOI":"10.1109\/ICEEA.2010.5596104"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Do, C.P., Le, Q.H., Pham, D.P., and Le, D.K. (2021, January 12\u201313). Forecast of Energy Consumption of Drying System According to The Environmental Temperature and Humidity on IoT by Arima Algorithm. Proceedings of the 2020 Applying New Technology in Green Buildings (ATiGB), Da Nang, Vietnam.","DOI":"10.1109\/ATiGB50996.2021.9423429"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rahman, M., Islam, A.S., Nadvi, S.Y.M., and Rahman, R.M. (2013, January 17\u201318). Comparative study of ANFIS and ARIMA model for weather forecasting in Dhaka. Proceedings of the 2013 International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, Bangladesh.","DOI":"10.1109\/ICIEV.2013.6572587"},{"key":"ref_19","unstructured":"National Institute of Standards and Technology (2020, November 18). Fire Dynamics | Fire Research Division, NIST, Available online: https:\/\/www.nist.gov\/el\/fire-research-division-73300\/firegov-fire-service\/fire-dynamics."},{"key":"ref_20","unstructured":"Hoehler, M., Su, J., Lafrance, P.S., Bundy, M., Kimball, A., Brandon, D., and \u00d6stman, B. (2018). SiF 2018-The 10th International Conference on Structures in Fire, Belfast, UK, New University of Ulster."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ishola, A.A., and Valles, D. (2022, January 6\u20139). Using Machine Learning and Regression Analysis to Classify and Predict Danger Levels in Burning Sites. Proceedings of the 2022 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA.","DOI":"10.1109\/AIIoT54504.2022.9817232"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s10618-012-0295-5","article-title":"Training and assessing classification rules with imbalanced data","volume":"28","author":"Menardi","year":"2014","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_23","unstructured":"Zhu, M.-h. (2011). Communication Systems and Information Technology: Selected Papers from the 2011 International Conference on Electric and Electronics (EEIC 2011) in Nanchang, China on 20\u201322 June 2011, Volume 4, Springer."},{"key":"ref_24","first-page":"1","article-title":"Machine learning from imbalanced data sets 101","volume":"Volume 68","author":"Provost","year":"2000","journal-title":"Proceedings of the AAAI\u20192000 Workshop on Imbalanced Data Sets"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Harirchian, E., Kumari, V., Jadhav, K., Raj Das, R., Rasulzade, S., and Lahmer, T. (2020). A machine learning framework for assessing seismic hazard safety of reinforced concrete buildings. Appl. Sci., 10.","DOI":"10.3390\/app10207153"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1080\/10903120802104029","article-title":"Threats to life in residential structure fires","volume":"12","author":"Cone","year":"2008","journal-title":"Prehospital Emerg. Care"},{"key":"ref_27","unstructured":"Mensch, A., Braga, G., and Bryner, N. (2022, April 05). Fire Exposures of Firefighter Self-Contained Breathing Apparatus Facepiece Lenses, Available online: https:\/\/tsapps.nist.gov\/publication\/get_pdf.cfm?pub_id=909917."},{"key":"ref_28","unstructured":"Yusuf, S., and Garrity, D. (2020). Prediction of Temperature Rise Event. (US10657801B2), U.S. Patent."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Feng, W., Chen, Z., and Fu, Y. (2018, January 19\u201323). Autoencoder classification algorithm based on swam intelligence optimization. Proceedings of the 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), Wuxi, China.","DOI":"10.1109\/DCABES.2018.00068"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/bs.adcom.2019.09.007","article-title":"Energy-efficient edge based real-time healthcare support system","volume":"117","author":"Abirami","year":"2020","journal-title":"Adv. Comput."},{"key":"ref_31","unstructured":"Altan, G., and Kutlu, Y. (2020). Deep Learning for Data Analytics, Academic Press."},{"key":"ref_32","unstructured":"Hyndman, R.J., and Athanasopoulos, G. (2018). Forecasting: Principles and Practice, OTexts."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jaradat, F.B., and Valles, D. (2020, January 6\u20138). A victims detection approach for burning building sites using convolutional neural networks. Proceedings of the 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC47524.2020.9031275"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4628\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:32:11Z","timestamp":1760124731000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4628"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,10]]},"references-count":33,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23104628"],"URL":"https:\/\/doi.org\/10.3390\/s23104628","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,10]]}}}