Critical event prediction using ontology-based sensor data integration and deep learning

Abstract

Event detection and prediction play a crucial role in various domains, particularly in early warning systems where identifying critical events in advance can prevent disasters and mitigate risks. This research addresses the problem of integrating heterogeneous sensor data, which is common in fields such as disaster management and smart cities, where raw data may not provide sufficient context for effective decision-making. To address this challenge, we propose a framework that combines an ontology-based approach to integrate and contextualize sensor data with deep learning techniques for event prediction. The framework utilizes an extended Semantic Sensor Network (SSN) ontology to structure and enrich the data with semantic and contextual information. Then, Long Short-Term Memory (LSTM) neural networks are applied to predict events based on the integrated data. A case study was conducted in the hydrological domain, using real-world hydrometric and hydrological sensor data to predict critical events such as floods. The results demonstrate that combining semantic integration with AI techniques improves prediction accuracy and enables the early detection of critical events that might be missed when using isolated data.