Using advanced data mining and integration in environmental risk management

Abstract

Environmental risk management research is an established part of the Earth sciences domain, already known for using powerful computational resources to model physical phenomena in the atmosphere, oceans, and rivers. In this paper we explore how these data-intensive processes can be managed by machine-learning and data mining techniques to benefit the experts who produce daily weather predictions, as well as rarely needed, but crucial and often time-critical risk assessments for emerging environmentally significant events. We illustrate the possibilities on a selected scenario from the hydro-meteorological domain, and then describe how this scenario could be extended to provide meteorologists and hydrologists with new data and insights currently not routinely available.