Environics Oy (Finland)

In this paper the long-term evolution of the catchment area of Eurajoki River, situated in Western Finland, is studied. The modelling area, nearly 1000 km2 in size, is at present mostly covered by sea. Probabilistic digital elevation model and land uplift model form the basis for the future catchment area modelling. A land uplift model is required due to the ongoing post-glacial rebound especially in the western parts of Finland. The maximum rate of land uplift in Finland is 1 cm per year while in the modelling area the land uplift rate is about 6 mm per year. The digital elevation model and land uplift model have been calculated using Monte Carlo simulation where the uncertainties in the source data have been taken into account. The probabilistic nature of these models enables also the river catchment area and river network analyses probabilistically. The analyses are done for the next 10,000 years in 1000-year intervals and 100 realizations are estimated for each time point. The results show that the catchment area expands towards the west as the land rises. An alternative river branch flowing northwards from the main course will form with a significant probability. Also, a delta area with multiple river branches is expected to form at about 7000 years after present.

The detection and identification of hazardous chemical agents are important problems in the fields of security and defense. Although the diverse environmental conditions and varying concentrations of the chemical agents make the problem challenging, the identification system should be able to give early warnings, identify the gas reliably, and operate with low false alarm rate. We have researched detection and identification of chemical agents with a swept-field aspiration condenser type ion mobility spectrometry prototype. This paper introduces an identification system, which consists of a cumulative sum algorithm (CUSUM) -based change detector and a neural network classifier. As a novelty, the use of CUSUM algorithm allows the gas identification task to be accomplished using carefully selected measurements. For the identification of hazardous agents we, as a further novelty, utilize the principal component analysis to transform the swept-field ion mobility spectra into a more compact and appropriate form. Neural networks have been found to be a reliable method for spectra categorization in the context of swept-field technology. However, the proposed spectra reduction raises the accuracy of the neural network classifier and decreases the number of neurons. Finally, we present comparison to the earlier neural network solution and demonstrate that the percentage of correctly classified sweeps can be considerably raised by using the CUSUM-based change detector.