Application for assisted VFR flight planning

The Flutter-based application developed in this study offers pilots a user-friendly interface, intuitive features, and real-time data integration. Through this application, pilots can easily create and modify flight routes, analyze weather patterns, assess airspace restrictions, and carry out simulations to obtain the most efficient route based on multiple parameters, as far as meteorology is concerned. Moreover, the application integrates with various databases and online resources to provide comprehensive data on airports, airspaces, and terrain, enabling pilots to make informed decisions during the flight planning phase. Additionally, this thesis aims to demonstrate that a competent software tool, capable of competing with other established flight planning tools, can be developed by a student. By utilizing the powerful Flutter framework, which provides a rich set of development tools and a vast community support, this research showcases the ability of a student to create a robust and feature-rich application for VFR flight planning. The successful development and evaluation of this application serve as a testament to the potential of aspiring developers to contribute innovative solutions to the aviation industry. Furthermore, this thesis highlights the importance of empowering students and encouraging their involvement in the digital transformation of traditional aviation processes, fostering creativity, and driving progress in the field.OutgoingObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::4 - Educació de Qualita

Assessing Relevance of Tweets for Risk Communication

Although Twitter is used for emergency management activities, the relevance of tweets during a hazard event is still open to debate. In this study, six different computational (i.e. Natural Language Processing) and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event. Primarily, tweets containing information about the flooding events and its impacts were analysed. Examination of the relationships between tweet volume and its content with precipitation amount, damage extent, and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages. However, only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event. By providing insight into the quality of social media data and its usefulness to emergency management activities, this study contributes to the literature on quality of big data. Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness

Predicting large scale fine grain energy consumption

Today a large volume of energy-related data have been continuously collected. Extracting actionable knowledge from such data is a multi-step process that opens up a variety of interesting and novel research issues across two domains: energy and computer science. The computer science aim is to provide energy scientists with cutting-edge and scalable engines to effectively support them in their daily research activities. This paper presents SPEC, a scalable and distributed predictor of fine grain energy consumption in buildings. SPEC exploits a data stream methodology analysis over a sliding time window to train a prediction model tailored to each building. The building model is then exploited to predict the upcoming energy consumption at a time instant in the near future. SPEC currently integrates the artificial neural networks technique and the random forest regression algorithm. The SPEC methodology exploits the computational advantages of distributed computing frameworks as the current implementation runs on Spark. As a case study, real data of thermal energy consumption collected in a major city have been exploited to preliminarily assess the SPEC accuracy. The initial results are promising and represent a first step towards predicting fine grain energy consumption over a sliding time window

Quantitative Estimation of Causality and Predictive Modeling for Precipitation Observation Sites and River Gage Sensors

This project seeks to investigate two questions: correlations from precipitation measurement sensors to river gage sensors, and predictive modeling of peak river gage heights during precipitation events. First, if correlations can be quantified, then a predictive model can be explored to predict peak water levels at river gage sensors, in response to precipitation inputs. Answering both research questions can provide early flood detection benefits and provide quantitative time assessments for flood risks. An extensive data-driven study was conducted across a geographical area of the U.S, spanning the time period 2008-2016 to identify river gage sensors that are closely correlated to nearby rainfall events. More than 1000 precipitation observation sites were identified and for each precipitation site, nearby river gage stations/sensors were ranked using a cross correlation measure. The cross correlation measures provide information such as which river gage sensors are most sensitive to nearby precipitation inputs. Predictive machine learning models were also developed around each rainfall-river gage pair to learn from historical rainfall and river gage levels, and then predict peak river gage heights. The predictive models generated were accurate and verified a strong causality between precipitation events and river gages that were sensitive to such events. A web-based and map-based decision support and visualization tool was also developed to depict the causality between precipitation and river gage sites and to graphically display the results of the predictive models. This study found about 3500 strongly correlated rain station and river gage pairs. Machine Learning models for these pairs yield high accuracy - 80 percent and above

LifeWatch observatory data : zooplankton observations in the Belgian part of the North Sea

Through regular sampling surveys, the Flanders Marine Institute (VLIZ) is generating a long-term data series for the Belgian coastal water and sandbank system, a designated site in the Long Term Ecological Research (LTER) network. The data series is built from sampling activities initiated in 2012 in the framework of the LifeWatch marine observatory. Nine nearshore stations are sampled monthly, with an additional eight offshore stations sampled seasonally. This paper presents the generated data series for zooplankton densities and size measurements, analysed using a ZooScan plankton imaging device together with the ZooProcess and Plankton Identifier software packages. To date 673.017 biological particles have been collected and identified. The collection and processing of the 2012-2018 dataset is described, along with its data curation and quality control. Yearly versions of the data are published in a standardized format together with environmental parameters, accompanied by an extensive metadata description and labelled with digital identifiers for traceability. The data are published under a CC-BY 4.0 license, allowing use of the data under the condition of providing the reference to the original source

Cloud-based Implementation and Validation of a Predictive Fire Risk Indication Model

The high representation of wooden houses in Norwegian cities combined with periods of dry and cold climate during the winter time often results in a high risk of severe fires. This makes it important for public authorities and fire departments to have an accurate estimate of the current fire risk in order to take proper precautions. We report on the implementation of a predictive mathematical model based on first order principles which exploits cloud-provided measurements from weather stations and weather forecasts from the Norwegian Meteorological Institute to predict the current and future fire risk at a given geographical location. We have experimentally validated the model during the winter 2018-2019 at selected geographical locations, and by considering weather data from the time of several historical fires. Our results show that our cloud and web-based implementation is both time and storage efficient, and capable of being able to accurately predict the fire risk measured in terms of the estimated time to ashover. The paper demonstrates that our methodology in the near future may become a valuable risk predicting tool for Norwegian fire brigades

Log File Analysis in Cloud with Apache Hadoop and Apache Spark

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015.Log files are a very important set of data that can lead to useful information through proper analysis. Due to the high production rate and the number of devices and software that generate logs, the use of cloud services for log analysis is almost necessary. This paper reviews the cloud computational framework ApacheTM Hadoop R, highlights the differences and similarities between Hadoop MapReduce and Apache SparkTM and evaluates the performance of them. Log file analysis applications were developed in both frameworks and performed SQL-type queries in real Apache Web Server log files. Various measurements were taken for each application and query with different parameters in order to extract safe conclusions about the performance of the two frameworks.The authors would like to thank Okeanos the GRNET’s cloud service for the valuable resources