Real-Time Railway Hazard Detection Using Distributed Acoustic Sensing and Hybrid Ensemble Learning

Highlights A hybrid Voting Classifier model was developed to accurately detect and classify environmental events affecting railway safety using fiber optic Distributed Acoustic Sensing (DAS). The system enables early detection of rockfall-related events (including small and medium debris slides) and tree obstructions along the Karab & uuml;k-Yenice railway line. What are the main findings? The system may detect rockfalls, tree obstructions, and landslides effectively with real-time Distributed Acoustic Sensing (DAS). The hybrid Voting Classifier model achieved 98% accuracy in classifying railway environmental events using fiber optic sensor data. What is the implication of the main finding? The model can improve railway safety by enabling a proactive response through early anomaly detection. It demonstrates scalability for use in harsh geographical areas and other infrastructure monitoring applications.Highlights A hybrid Voting Classifier model was developed to accurately detect and classify environmental events affecting railway safety using fiber optic Distributed Acoustic Sensing (DAS). The system enables early detection of rockfall-related events (including small and medium debris slides) and tree obstructions along the Karab & uuml;k-Yenice railway line. What are the main findings? The system may detect rockfalls, tree obstructions, and landslides effectively with real-time Distributed Acoustic Sensing (DAS). The hybrid Voting Classifier model achieved 98% accuracy in classifying railway environmental events using fiber optic sensor data. What is the implication of the main finding? The model can improve railway safety by enabling a proactive response through early anomaly detection. It demonstrates scalability for use in harsh geographical areas and other infrastructure monitoring applications.Highlights A hybrid Voting Classifier model was developed to accurately detect and classify environmental events affecting railway safety using fiber optic Distributed Acoustic Sensing (DAS). The system enables early detection of rockfall-related events (including small and medium debris slides) and tree obstructions along the Karab & uuml;k-Yenice railway line. What are the main findings? The system may detect rockfalls, tree obstructions, and landslides effectively with real-time Distributed Acoustic Sensing (DAS). The hybrid Voting Classifier model achieved 98% accuracy in classifying railway environmental events using fiber optic sensor data. What is the implication of the main finding? The model can improve railway safety by enabling a proactive response through early anomaly detection. It demonstrates scalability for use in harsh geographical areas and other infrastructure monitoring applications.Abstract Rockfalls on railways are considered a natural disaster under the topic of landslides. It is an event that varies regionally due to landforms and climate. In addition to traffic density, the Karab & uuml;k-Yenice railway line also passes through mountainous areas, river crossings, and experiences heavy seasonal rainfall. These conditions necessitate the implementation of proactive measures to mitigate risks such as rockfalls, tree collapses, landslides, and other geohazards that threaten the railway line. Undetected environmental events pose a significant threat to railway operational safety. The study aims to provide early detection of environmental phenomena using vibrations emitted through fiber optic cables. This study presents a real-time hazard detection system that integrates Distributed Acoustic Sensing (DAS) with a hybrid ensemble learning model. Using fiber optic cables and the Luna OBR-4600 interrogator, the system captures environmental vibrations along a 6 km railway corridor in Karab & uuml;k, T & uuml;rkiye. CatBoosting, Support Vector Machine (SVM), LightGBM, Decision Tree, XGBoost, Random Forest (RF), and Gradient Boosting Classifier (GBC) algorithms were used to detect the incoming signals. However, the Voting Classifier hybrid model was developed using SVM, RF, XGBoost, and GBC algorithms. The signaling system on the railway line provides critical information for safety by detecting environmental factors. Major natural disasters such as rockfalls, tree falls, and landslides cause high-intensity vibrations due to environmental factors, and these vibrations can be detected through fiber cables. In this study, a hybrid model was developed with the Voting Classifier method to accurately detect and classify vibrations. The model leverages an ensemble of classification algorithms to accurately categorize various environmental disturbances. The system has proven its effectiveness under real-world conditions by successfully detecting environmental events such as rockfalls, landslides, and falling trees with 98% success for Precision, Recall, F1 score, and accuracy.