Abstract: Southern European countries are severely affected by forest fires every year, which lead to very large environmental damages and great economic investments to recover affected areas. All affected countries invest lots of resources to minimize fire damages. Emerging technologies are used to help wildfire analysts determine fire behavior and spread aiming at a more efficient use of resources in fire fighting. In the case of trans-boundary fires, the European Forest Fire Information System (EFFIS) works as a complementary system to national and regional systems in the countries, providing information required for international collaboration on forest fire prevention and fighting. In this work, we describe a way of exploiting all the available information in the system to feed a dynamic data driven wildfire behavior prediction model that can deliver results to support operational decisions. The model is able to calibrate the unknown parameters based on the real observed data, such as wind condition and fuel moistures, using a steering loop. Since this process is computational intensive, we exploit multi-core platforms using a hybrid MPI-OpenMP programming paradigm.

Abstract: In this paper we present new ideas to greatly enhance the quality of uncertainty quantification in the DDDAS framework. We build on ongoing work in large scale transport of geophysical mass of volcanic origin -- a danger to both land based installations and airborne vehicles.

Abstract: We present a dynamic data-driven decision support for aquaculture farm closure. In decision support, we use machine learning techniques in predicting closures of a shellfish farm. As environmental time series are used in closure, we propose two approaches using time series and machine learning for closure prediction. In one approach, we consider time series prediction and then using expert rules to predict closure. In other approach, we use time series classification for closure prediction. Both approaches exploit a dynamic data-driven technique where prediction models are updated with the update of new data to predict closure decisions. Experimental results at a case study shellfish farm validate the applicability of the proposed method in aquaculture decision support.

Abstract: In this paper, we outline key features that dynamic data-driven application systems (DDDAS) have. The term Big Data (BD) has come into being in recent years that is highly applicable to most DDDAS since most applications use networks of sensors that generate an overwhelming amount of data in the lifespan of the application runs. We describe what a dynamic big-data-driven application system (DBDDAS) toolkit must have in order to provide all of the essential building blocks that are necessary to easily create new DDDAS without re-inventing the building blocks.