{"id":{"repo_id":"texas-state","oai_identifier":"oai:digital.library.txst.edu:10877/20003"},"canonical_url":"https://search.dev.ndltd.org/etd/texas-state/oai:digital.library.txst.edu:10877/20003","repository":{"repo_id":"texas-state","name":"Texas State University","base_url":"https://digital.library.txst.edu/server/oai/request"},"display":{"title":"Using Machine Learning to Classify Danger Levels and Prediction Techniques to Forecast Temperature Changes with Height in Burning Sites","abstract":"Firefighters go into burning structures to rescue trapped victims and extinguish the fire as soon as possible. Factors such as extreme temperatures, smoke, toxic gases, explosions, and falling objects inhibit their efficiency and risk their safety. These factors could change within a twinkle of an eye. Firefighters must be provided with accurate information and data about the burning site. They can make informed decisions about their duties and know when it is safe to enter and evacuate to reduce casualties. This research presents Machine Learning (ML) algorithms for classifying the danger levels and prediction models to forecast temperature changes with height and time in burning sites using environmental factors such as temperature, smoke, and carbon monoxide, CO. The classifier algorithms bring the chief firefighter’s awareness of the danger levels in the burning compartment. The predictor models forecast the rise in temperature from 0.6m to 2.6m and the changes in temperature with time at 2.6m. Knowing the temperature at 2.6m is essential because the temperature rises faster with height and the elevated temperature at this height weakens the building's structural members. We investigated four techniques for classification and used data analysis prediction methods to predict temperature parameters. Classification methods covered the support vector machine (SVM), logistic regression (LR), k-nearest neighbors (k-NN), and autoencoderartificial neural network (AE-ANN). The prediction data analytical approach covered ARIMA with random forest regression implementation. Analysis showed that AE-ANN performed poorly in classifying the latent representation of our custom environmental data into dangerous levels, and SVM outperformed the other ML models. The ARIMA model excellently predicts the trend of temperature changes in the burning site. Still, the errors are a bit high due to significant variations in the decay phase of fire development stages exhibited by different fire grounds.","abstract_html":"Firefighters go into burning structures to rescue trapped victims and extinguish the fire as soon as possible. Factors such as extreme temperatures, smoke, toxic gases, explosions, and falling objects inhibit their efficiency and risk their safety. These factors could change within a twinkle of an eye. Firefighters must be provided with accurate information and data about the burning site. They can make informed decisions about their duties and know when it is safe to enter and evacuate to reduce casualties. This research presents Machine Learning (ML) algorithms for classifying the danger levels and prediction models to forecast temperature changes with height and time in burning sites using environmental factors such as temperature, smoke, and carbon monoxide, CO. The classifier algorithms bring the chief firefighter’s awareness of the danger levels in the burning compartment. The predictor models forecast the rise in temperature from 0.6m to 2.6m and the changes in temperature with time at 2.6m. Knowing the temperature at 2.6m is essential because the temperature rises faster with height and the elevated temperature at this height weakens the building's structural members. We investigated four techniques for classification and used data analysis prediction methods to predict temperature parameters. Classification methods covered the support vector machine (SVM), logistic regression (LR), k-nearest neighbors (k-NN), and autoencoderartificial neural network (AE-ANN). The prediction data analytical approach covered ARIMA with random forest regression implementation. Analysis showed that AE-ANN performed poorly in classifying the latent representation of our custom environmental data into dangerous levels, and SVM outperformed the other ML models. The ARIMA model excellently predicts the trend of temperature changes in the burning site. Still, the errors are a bit high due to significant variations in the decay phase of fire development stages exhibited by different fire grounds.","abstract_has_math":false,"creators":["Ishola, Adenrele"],"institution":"Texas State University","degree_name":"Master of Science","degree_level":"Masters","degree_discipline":"Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Valles, Damian"],"committee_chairs":[],"committee_members":["Stapleton, William","Aslan, Semih"],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-27T21:22:28Z","subjects":["firefighting","machine learning","classifications","predictions","SVM","logistic regression","KNN","ARIMA"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10877/20003","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Valles, Damian"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Stapleton, William","Aslan, Semih"]},{"key":"dc:creator","label":"Author","values":["Ishola, Adenrele"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-12-11T19:39:55Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-12-11T19:39:55Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-12"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Texas State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["firefighting","machine learning","classifications","predictions","SVM","logistic regression","KNN","ARIMA"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10877/20003"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Firefighters go into burning structures to rescue trapped victims and extinguish the fire as soon as possible. Factors such as extreme temperatures, smoke, toxic gases, explosions, and falling objects inhibit their efficiency and risk their safety. These factors could change within a twinkle of an eye. Firefighters must be provided with accurate information and data about the burning site. They can make informed decisions about their duties and know when it is safe to enter and evacuate to reduce casualties. This research presents Machine Learning (ML) algorithms for classifying the danger levels and prediction models to forecast temperature changes with height and time in burning sites using environmental factors such as temperature, smoke, and carbon monoxide, CO. The classifier algorithms bring the chief firefighter’s awareness of the danger levels in the burning compartment. The predictor models forecast the rise in temperature from 0.6m to 2.6m and the changes in temperature with time at 2.6m. Knowing the temperature at 2.6m is essential because the temperature rises faster with height and the elevated temperature at this height weakens the building's structural members. We investigated four techniques for classification and used data analysis prediction methods to predict temperature parameters. Classification methods covered the support vector machine (SVM), logistic regression (LR), k-nearest neighbors (k-NN), and autoencoderartificial neural network (AE-ANN). The prediction data analytical approach covered ARIMA with random forest regression implementation. Analysis showed that AE-ANN performed poorly in classifying the latent representation of our custom environmental data into dangerous levels, and SVM outperformed the other ML models. The ARIMA model excellently predicts the trend of temperature changes in the burning site. Still, the errors are a bit high due to significant variations in the decay phase of fire development stages exhibited by different fire grounds."]},{"key":"dc:format","label":"Dc Format","values":["Text"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["1 file (.pdf)"]},{"key":"dc:title","label":"Title","values":["Using Machine Learning to Classify Danger Levels and Prediction Techniques to Forecast Temperature Changes with Height in Burning Sites"]}]}],"canonical_facts":{"dc:contributor.advisor":["Valles, Damian"],"dc:contributor.committeemember":["Stapleton, William","Aslan, Semih"],"dc:creator":["Ishola, Adenrele"],"dc:date.accessioned":["2024-12-11T19:39:55Z"],"dc:date.available":["2024-12-11T19:39:55Z"],"dc:date.issued":["2022-12"],"dc:description.abstract":["Firefighters go into burning structures to rescue trapped victims and extinguish the fire as soon as possible. Factors such as extreme temperatures, smoke, toxic gases, explosions, and falling objects inhibit their efficiency and risk their safety. These factors could change within a twinkle of an eye. Firefighters must be provided with accurate information and data about the burning site. They can make informed decisions about their duties and know when it is safe to enter and evacuate to reduce casualties. This research presents Machine Learning (ML) algorithms for classifying the danger levels and prediction models to forecast temperature changes with height and time in burning sites using environmental factors such as temperature, smoke, and carbon monoxide, CO. The classifier algorithms bring the chief firefighter’s awareness of the danger levels in the burning compartment. The predictor models forecast the rise in temperature from 0.6m to 2.6m and the changes in temperature with time at 2.6m. Knowing the temperature at 2.6m is essential because the temperature rises faster with height and the elevated temperature at this height weakens the building's structural members. We investigated four techniques for classification and used data analysis prediction methods to predict temperature parameters. Classification methods covered the support vector machine (SVM), logistic regression (LR), k-nearest neighbors (k-NN), and autoencoderartificial neural network (AE-ANN). The prediction data analytical approach covered ARIMA with random forest regression implementation. Analysis showed that AE-ANN performed poorly in classifying the latent representation of our custom environmental data into dangerous levels, and SVM outperformed the other ML models. The ARIMA model excellently predicts the trend of temperature changes in the burning site. Still, the errors are a bit high due to significant variations in the decay phase of fire development stages exhibited by different fire grounds."],"dc:format":["Text"],"dc:format.medium":["1 file (.pdf)"],"dc:identifier.uri":["https://hdl.handle.net/10877/20003"],"dc:language.iso":["en"],"dc:subject":["firefighting","machine learning","classifications","predictions","SVM","logistic regression","KNN","ARIMA"],"dc:title":["Using Machine Learning to Classify Danger Levels and Prediction Techniques to Forecast Temperature Changes with Height in Burning Sites"],"dc:type":["Thesis"],"thesis:degree_discipline":["Engineering"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Texas State University"]},"updated_at":"2026-07-27T21:22:28Z"}