5 research outputs found
Innovative big data integrationand analysis techniques for urban hazard management
PhD ThesisModern early warning systems (EWS) require sophisticated knowledge of natural hazards,
the urban context and underlying risk factors to enable dynamic and timely
decision making (e.g., hazard detection, hazard preparedness). Landslides are a common
form of natural hazard with a global impact and are closely linked to a variety of
other hazards. EWS for landslide prediction and detection relies on scienti c methods
and models which require input from the time-series data, such as the earth observation
(EO) and ancillary data. Such data sets are produced by a variety of remote sensing
satellites and Internet of Things sensors which are deployed in landslide-prone areas.
Besides, social media-based time-series data has played a signi cant role in modern
disaster management. The emergence of social media has led to the possibility of the
general public contributing to the monitoring of natural hazard by reporting incidents
related to hazard events. To this end, the data integration and analysis of potential
time-series data sources in EWS applications have become a challenge due to the complexity
and high variety of data sources. Moreover, sophisticated domain knowledge of
natural hazards and risk management are also required to enable dynamic and timely
decision making about serious hazards. In this thesis, a comprehensive set of algorithmic
techniques for managing high varieties of time series data from heterogeneous
data sources is investigated. A novel ontology, namely Landslip Ontology, is proposed
to provide a knowledge base that establishes the relationship between landslide hazard
and EO and ancillary data sources to support data integration for EWS applications.
Moreover, an ontology-based data integration and analytics system that includes human
in the loop of hazard information acquisition from social media is proposed to
establish a deeper and more accurate situational awareness of hazard events. Finally,
the system is extended to enable an interaction between natural hazard EWS and
electrical grid EWS to contribute to electrical grid network monitoring and support
decision-making for electrical grid infrastructure management