Ensemble methods for spatial data stream classification with application to emergency services

Our research investigates the application of ensemble methods for spatial data stream classification within emergency services, specifically severe weather events. Emergencies demand swift, accurate decisions to mitigate impacts and protect lives. Ensemble methods improve the accuracy of predictions by combining outputs from multiple neural networks, each trained on diverse aspects of the data, including geographic coordinates, weather data, spatial data, and logistical factors. These models collectively contribute to more precise decision-making, particularly in assessing evacuation priorities. We collected and generated relevant data for affected regions and evacuation centres pertinent to severe weather events. For class labeling data, we employed K-means and DBSCAN clustering techniques. Our findings show that the ensemble of neural networks significantly improves the classification accuracy of spatial data stream data, potentially leading to more effective emergency responses. Comprehensive experiments with streams containing both spatial and non-spatial data show the accuracy, precision, and recall of our proposed approach.

Authors

• Bhattacharjee, Prasanta
• University of Lethbridge. Faculty of Arts and Science

• ensemble methods
• spatial data stream classification
• emergency services
• predicting severe weather events
• neural network ensembles

Identifier

hdl:10133/6961

OAI identifier oai:identifier

oai:opus.uleth.ca:10133/6961