4 research outputs found
Mining Explainable Predictive Features for Water Quality Management
With water quality management processes, identifying and interpreting
relationships between features, such as location and weather variable tuples,
and water quality variables, such as levels of bacteria, is key to gaining
insights and identifying areas where interventions should be made. There is a
need for a search process to identify the locations and types of phenomena that
are influencing water quality and a need to explain how the quality is being
affected and which factors are most relevant. This paper addresses both of
these issues. A process is developed for collecting data for features that
represent a variety of variables over a spatial region and which are used for
training models and inference. An analysis of the performance of the features
is undertaken using the models and Shapley values. Shapley values originated in
cooperative game theory and can be used to aid in the interpretation of machine
learning results. Evaluations are performed using several machine learning
algorithms and water quality data from the Dublin Grand Canal basin
Giving mobile devices a SIXTH sense: Introducing the SIXTH middleware for Augmented Reality applications
With the increasing availability of sensors within smartphones and within the world at large, a question arises about how this sensor data can be leveraged by Augmented Reality (AR) devices. AR devices have traditionally been limited by the capability of a given device's unique set of sensors. Connecting sensors from multiple devices using a Sensor Web could address this problem. Through leveraging this SensorWeb existing AR environments could be improved and new scenarios made possible, with devices that previously could not have being used as part of an AR environment. This paper proposes the use of SIXTH: a middleware designed to generate a Sensor Web, which allows a device to leverage heterogeneous external sensors within its environment to help facilitate the creation of richer AR experiences. This paper will present a worst case scenario, in which the device chosen will be a see-through, Android-based Head Mounted Display that has no access to sensors. This device is transformed into an AR device through the creation of a Sensor Web allowing it to sense its environment facilitated through the use of SIXTH
The MĂRA Data Extraction toolkit
Abstract Historical meteorological datasets are indispensable for forming climatic models and the generation of weather forecasts. Such data are core to the training phase of prediction models and may also be harnessed for hydrological and environmental models. GRIB is the most common data format used in meteorology and represents the de facto standard for storing historical weather data. However, GRIB datasets are complex and do not constitute analysisâready data without additional preprocessing. The MĂRA dataset of the Met Eireann, the Irish meteorological service, is archetypical of such datasets, emerging from a highâresolution climatic reanalysis of Irish weather data between 1981 and 2019. This article describes the MĂRA Data Extractor toolkit. This toolkit enables the intuitive, fast extraction and preprocessing of data from this extensive dataset. The toolkit is available as open source and will be of interest to those researching climate modelling in Europe