Applying Machine Learning to Catalogue Matching in Astrophysics

We present the results of applying automated machine learning techniques to the problem of matching different object catalogues in astrophysics. In this study we take two partially matched catalogues where one of the two catalogues has a large positional uncertainty. The two catalogues we used here were taken from the HI Parkes All Sky Survey (HIPASS), and SuperCOSMOS optical survey. Previous work had matched 44% (1887 objects) of HIPASS to the SuperCOSMOS catalogue. A supervised learning algorithm was then applied to construct a model of the matched portion of our catalogue. Validation of the model shows that we achieved a good classification performance (99.12% correct). Applying this model, to the unmatched portion of the catalogue found 1209 new matches. This increases the catalogue size from 1887 matched objects to 3096. The combination of these procedures yields a catalogue that is 72% matched.Comment: 8 Pages, 5 Figure

Experimentally Driven Guaranteed Parameter Estimation: a Way to Speed up Model-Based Design of Experiments Techniques

Parameter estimation in modelling reaction kinetics is affected by the prior knowledge on the domain of variability of model parameters which can be very limited at the beginning of model building activities. In conventional parameter estimation approaches a reasonably wide domain of variability for kinetic parameters is initially assumed, but this uncertainty on domain definition might deeply affect the efficiency of model-based experimental design techniques for model validation. In this work, we propose the use of binary classification techniques to define a feasible parametric region of parameter variability satisfying a set of user-defined model-based constraints. The proposed approach is illustrated in a case study of consecutive reactions in a plug flow reactor

Cognitive based decision support for water management and catchment regulation

The effect of climate change on water ecosystems include increased winter precipitation, severe floods, leading to fluctuations in stream flow in areas and affecting both fish survival and water supplies. Several methods exist for establishing projections of changes in precipitation with regards to river flows and water levels at the river-basin scale, but hydrological characteristics change remain difficult to predict. Ensuring optimization techniques for water systems becomes significantly important especially with the degradation of water ecosystems and increased risks for fish population. On the other hand, water demand has increased in the recent periods with the population growth. Further changes in the irrigation water system demand are determined by climate change precluding the reliability of current water management systems and affecting on the water-related ecosystems. To address these challenges real time water management and optimization strategies are required to facilitate a more autonomous management process that can address requirements for water demand, supply and ecosystem preservation. We present a cognitive based decision system that performs river level prediction for water optimization and catchment regulation for preserving Usk reservoir ecosystem in South Wales. The research is conducted on the Usk reservoir in South Wales reservation that is seeking to preserve the ecosystem and for which we propose a more informed decision system for catchment regulation and water management. Our system provides five days river level prediction to regulate river levels by pumping from/to reservoirs and to create artificial spates during the salmon migration season and to coincide with periods of low river flow