Data fusion with Gaussian processes for estimation of environmental hazard events

Abstract

Environmental hazard events such as extra‐tropical cyclones or windstorms that develop in the North Atlantic can cause severe societal damage. Environmental hazard is quantified by the hazard footprint, a spatial area describing potential damage. However, environmental hazards are never directly observed, so estimation of the footprint for any given event is primarily reliant on station observations (e.g., wind speed in the case of a windstorm event) and physical model hindcasts. Both data sources are indirect measurements of the true footprint, and here we present a general statistical framework to combine the two data sources for estimating the underlying footprint. The proposed framework extends current data fusion approaches by allowing structured Gaussian process discrepancy between physical model and the true footprint, while retaining the elegance of how the "change of support" problem is dealt with. Simulation is used to assess the practical feasibility and efficacy of the framework, which is then illustrated using data on windstorm Imogen.

Information Quality Assessment for Data Fusion Systems

“…In this regard, data fusion has gained much attention and been used in many applications in different fields such as sensor networks [8], fault diagnosis in maintenance [9], internet of things [10], eye movement recognition [11], economic data analysis [12], environmental hazard events [13], acoustic sensor networks [14], target tracking [15], robotics [16], image processing [17], intelligent systems designing, health applications [18], biometrics [19], surveillance [20], and human capital [21] approaches. In this study, the terms "information fusion" and "data fusion" are employed indistinctly for simplicity purposes.…”