Analytics and Decision Support for Green IS and Sustainability Applications
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ItemBridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network( 2020-01-07)An automatic system that utilizes data analytics and machine learning to identify adult American eel in data obtained by imaging sonars is created in this study. Wavelet transform has been applied to de-noise the ARIS sonar data and a convolutional neural network model has been built to classify eels and non-eel objects. Because of the unbalanced amounts of data in laboratory and field experiments, a transfer learning strategy is implemented to fine-tune the convolutional neural network model so that it performs well for both the laboratory and field data. The proposed system can provide important information to develop mitigation strategies for safe passage of out-migrating eels at hydroelectric facilities.
ItemData Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices( 2020-01-07)There are growing opportunities to leverage new technologies and data sources to address global problems related to sustainability, climate change, and biodiversity loss. The emerging discipline of GeoAI resulting from the convergence of AI and Geospatial science (Geo-AI) is enabling the possibility to harness the increasingly available open Earth Observation data collected from different constellations of satellites and sensors with high spatial, spectral and temporal resolutions. However, transforming these raw data into high-quality datasets that could be used for training AI and specifically deep learning models are technically challenging. This paper describes the process and results of synthesizing labelled-datasets that could be used for training AI (specifically Convolutional Neural Networks) models for determining agricultural land use pattern to support decisions for sustainable farming. In our opinion, this work is a significant step forward in addressing the paucity of usable datasets for developing scalable GeoAI models for sustainable agriculture.
ItemDriving Sustainably – The Influence of IoT-based Eco-Feedback on Driving Behavior( 2020-01-07)One starting point to reduce harmful greenhouse gas emissions is driving behavior. Previous studies have already shown that eco-feedback leads to reduced fuel consumption. However, less has been done to investigate how driving behavior is affected by eco-feedback. Yet, understanding driving behavior is important to target personalized recommendations towards re-duced fuel consumption. In this paper, we investigate a real-world data set from an IoT-based smart vehicle service. We first extract seven distinct factors that characterize driving behavior from data of 5,676 users. Second, we derive initial hypotheses on how eco-feedback may affect these factors. Third, we test these hypotheses with data of another 495 users receiving eco-feedback. Results suggest that eco-feedback, for instance, reduces hard acceleration maneuvers while interestingly speed is not affected. Our contribution extends the understanding of measuring driving behavior using IoT-based data. Furthermore, we contribute to a better understanding of the effect of eco-feedback on driving behavior.
ItemGreen IS Does Not Just Save Energy – Insights from a Survey on Organizations’ Uses of Sustainable Technologies( 2020-01-07)Organizations are increasingly challenged to digitally transform themselves, and to respond to calls for increased sustainability. While the adoption of sustainable innovations, such as Green information systems (Green IS), are one way to address both challenges, there are only few insights that provide nontechnology or company-specific insights into specific positive and negative Green IS outcomes. We address this shortcoming and shed light on Green IS adoption outcomes, as well as their interconnection to general sustainability initiatives in organizations. In a descriptive survey we find that many organizations already employ sustainability principles, but few incorporate Green IS. We confirm organizations almost exclusively report positive outcomes of Green IS usage, such as reduced resource consumption, increased compliance with regulations, and social acceptance. Based on these findings we suggest to especially further research the process of Green IS adoption.