EmC-ICDSST 2019: 5th International Conference on Decision Support System Technology - ICDSST 2019 & EURO Mini Conference 2019 on "Decision Support Systems: Main Developments & Future Trends"

info:eu-repo/semantics/publishedVersio

Assessment of climate change and development of data based prediction models of sediment yields in Upper Indus Basin

Hohe Raten von Sedimentflüssen und ihre Schätzungen in Flusseinzugsgebieten erfordern die Auswahl effizienter Quantifizierungsansätze mit einem besseren Verständnis der dominierten Faktoren, die den Erosionsprozess auf zeitlicher und räumlicher Ebene steuern. Die vorherige Bewertung von Einflussfaktoren wie Abflussvariation, Klima, Landschaft und Fließprozess ist hilfreich, um den geeigneten Modellierungsansatz zur Quantifizierung der Sedimenterträge zu entwickeln. Einer der schwächsten Aspekte bei der Quantifizierung der Sedimentfracht ist die Verwendung traditioneller Beziehung zwischen Strömungsgeschwindigkeit und Bodensatzlöschung (SRC), bei denen die hydrometeorologischen Schwankungen, Abflusserzeugungsprozesse wie Schneedecke, Schneeschmelzen, Eisschmelzen usw. nicht berücksichtigt werden können. In vielen Fällen führt die empirische Q-SSC Beziehung daher zu ungenauen Prognosen. Heute können datenbasierte Modelle mit künstlicher Intelligenz die Sedimentfracht präziser abschätzen. Die datenbasierten Modelle lernen aus den eingespeisten Datensätzen, indem sie bei komplexen Phänomenen wie dem Sedimenttransport die geeignete funktionale Beziehung zwischen dem Output und seinen Input-Variablen herstellen. In diesem Zusammenhang wurden die datenbasierten Modellierungsalgorithmen in der vorliegenden Forschungsarbeit am Lehrstuhl für Wasser- und Flussgebietsmanagement des Karlsruher Instituts für Technologie in Karlsruhe entwickelt, die zur Vorhersage von Sedimenten in oberen unteren Einzugsgebieten des oberen Indusbeckens von Pakistan (UIB) verwendet wurden. Die dieser Arbeit zugrunde liegende Methodik gliedert sich in vier Bearbeitungsschritte: (1) Vergleichende Bewertung der räumlichen Variabilität und der Trends von Abflüssen und Sedimentfrachten unter dem Einfluss des Klimawandels im oberen Indus-Becken (2) Anwendung von Soft-Computing-Modellen mit Eingabevektoren der schneedeckten Fläche zusätzlich zu hydro-klimatischen Daten zur Vorhersage der Sedimentfracht (3) Vorhersage der Sedimentfracht unter Verwendung der NDVI-Datensätze (Hydroclimate and Normalized Difference Vegetation Index) mit Soft-Computing-Modellen (4) Klimasignalisierung bei suspendierten Sedimentausträge aus Gletscher und Schnee dominierten Teileinzugsgebeiten im oberen Indus-Becken (UIB). Diese im UIB durchgeführte Analyse hat es ermöglicht, die dominiertenden Parameter wie Schneedecke und hydrologischen Prozesses besser zu und in eine verbesserte Prognose der Sedimentfrachten einfließen zu lassen. Die Analyse der Bewertung des Klimawandels von Flüssen und Sedimenten in schnee- und gletscherdominierten UIB von 13 Messstationen zeigt, dass sich die jährlichen Flüsse und suspendierten Sedimente am Hauptindus in Besham Qila stromaufwärts des Tarbela-Reservoirs im ausgeglichenen Zustand befinden. Jedoch, die jährlichen Konzentrationen suspendierter Sedimente (SSC) wurden signifikant gesenkt und lagen zwischen 18,56% und 28,20% pro Jahrzehnt in Gilgit an der Alam Bridge (von Schnee und Gletschern dominiertes Becken), Indus in Kachura und Brandu in Daggar (von weniger Niederschlag dominiertes Becken). Während der Sommerperiode war der SSC signifikant reduziert und lag zwischen 18,63% und 27,79% pro Jahrzehnt, zusammen mit den Flüssen in den Regionen Hindukush und West-Karakorum aufgrund von Anomalien des Klimawandels und im unteren Unterbecken mit Regen aufgrund der Niederschlagsreduzierung. Die SSC während der Wintersaison waren jedoch aufgrund der signifikanten Erwärmung der durchschnittlichen Lufttemperatur signifikant erhöht und lagen zwischen 20,08% und 40,72% pro Jahrzehnt. Die datenbasierte Modellierung im schnee und gletscherdominierten Gilgit Teilbecken unter Verwendung eines künstlichen neuronalen Netzwerks (ANN), eines adaptiven Neuro-Fuzzy-Logik-Inferenzsystems mit Gitterpartition (ANFIS-GP) und eines adaptiven Neuro-Fuzzy-Logik-Inferenzsystems mit subtraktivem Clustering (ANFIS) -SC), ein adaptives Neuro-Fuzzy-Logik- Inferenzsystem mit Fuzzy-C-Mittel-Clustering, multiplen adaptiven Regressionssplines (MARS) und Sedimentbewertungskurven (SRC) durchgeführt. Die Ergebnisse von Algorithmen für maschinelles Lernen zeigen, dass die Eingabekombination aus täglichen Abflüssen (Qt), Schneedeckenfläche (SCAt), Temperatur (Tt-1) und Evapotranspiration (Evapt-1) die Leistung der Sedimentvorhersagemodelle verbesserne. Nach dem Vergleich der Gesamtleistung der Modelle schnitt das ANN-Modell besser ab als die übrigen Modelle. Bei der Vorhersage der Sedimentfrachten in Spitzenzeiten lag die Vorhersage der ANN-, ANIS-FCM- und MARS-Modelle näher an den gemessenen Sedimentbelastungen. Das ANIS-FCM-Modell mit einem absoluten Gesamtfehler von 81,31% schnitt bei der Vorhersage der Spitzensedimente besser ab als ANN und MARS mit einem absoluten Gesamtfehler von 80,17% bzw. 80,16%. Die datenbasierte Modellierung der Sedimentfrachten im von Regen dominierten Brandu-Teilbecken wurde unter Verwendung von Datensätzen für Hydroklima und biophysikalische Eingaben durchgeführt, die aus Strömungen, Niederschlag, mittlerer Lufttemperatur und normalisiertem Differenzvegetationsindex (NDVI) bestehen. Die Ergebnisse von vier ANNs (Artificial Neural Networks) und drei ANFIS-Algorithmen (Adaptive Neuro-Fuzzy Logic Inference System) für das Brandu Teilnbecken haben gezeigt, dass der mittels Fernerkundung bestimmte NDVI als biophysikalische Parameter zusätzlich zu den Hydroklima-Parametern die Leistung das Modell nicht verbessert. Der ANFIS-GP schnitt in der Testphase besser ab als andere Modelle mit einer Eingangskombination aus Durchfluss und Niederschlag. ANN, eingebettet in Levenberg-Marquardt (ANN-LM) für den Zeitraum 1981-2010, schnitt jedoch am besten mit Eingabekombinationen aus Strömungen, Niederschlag und mittleren Lufttemperaturen ab. Die Ergebnisgenauigkeit R2 unter Verwendung des ANN-LM-Algorithmus verbesserte sich im Vergleich zur Sedimentbewertungskurve (SRC) um bis zu 28%. Es wurde gezeigt, dass für den unteren Teil der UIB-Flüsse Niederschlag und mittlere Lufttemperatur dominierende Faktoren für die Vorhersage von Sedimenterträgen sind und biophysikalische Parameter (NDVI) eine untergeordnete Rolle spielen. Die Modellierung zur Bewertung der Änderungen des SSC in schnee- und gletschergespeiste Gilgit- und Astore-Teilbecken wurde unter Verwendung des Temp-Index degree day modell durchgeführt. Die Ergebnisse des Mann-Kendall-Trendtests in den Flüssen Gilgit und Astore zeigten, dass der Anstieg des SSC während der Wintersaison auf die Erwärmung der mittleren Lufttemperatur, die Zunahme der Winterniederschläge und die Zunahme der Schneeschmelzen im Winter zurückzuführen ist. Während der Frühjahrssaison haben die Niederschlags- und Schneedeckenanteile im Gilgit-Unterbecken zugenommen, im Gegensatz zu seiner Verringerung im Astore-Unterbecken. Im Gilgit-Unterbecken war der SSC im Sommer aufgrund des kombinierten Effekts der Karakorum-Klimaanomalie und der vergrößerten Schneedecke signifikant reduziert. Die Reduzierung des Sommer-SSC im Gilgit Fluss ist auf die Abkühlung der Sommertemperatur und die Bedeckung der exponierten proglazialen Landschaft zurückzuführen, die auf erhöhten Schnee, verringerte Trümmerflüsse Trümmerflüsse und verringerte Schneeschmelzen von Trümmergletschern zurückzuführen sind. Im Gegensatz zum Gilgit River sind die SSC im Astore River im Sommer erhöht. Der Anstieg des SSC im Astore-Unterbecken ist auf die Verringerung des Frühlingsniederschlags und der Schneedecke, die Erwärmung der mittleren Sommerlufttemperatur und den Anstieg des effektiven Niederschlags zurückzuführen. Die Ergebnisse zeigen ferner eine Verschiebung der Dominanz von Gletscherschmelzen zu Schneeschmelzen im Gilgit-Unterbecken und von Schnee zu Niederschlägen im Astore-Unterbecken bei Sedimenteden Sedimentfrachten in UIB. Die vorliegende Forschungsarbeit zur Bewertung der klimabedingten Veränderungen des SSC und seiner Vorhersage sowohl in den oberen als auch in den unteren Teilbecken des UIB wird nützlich sein, um den Sedimenttransportprozess besser zu verstehen und aufbauen auf dem verbessertenProzessverständnis ein angepasstes Sedimentmanagement und angepasste Planungen der zukünftigen Wasserinfrastrukturen im UIB ableiten zu können

A Hydropower Facility as an Energy Water Signal Processor

In recent times, various efforts have been made to address the challenge of adequately representing hydropower systems in modeling frameworks, accounting for the lack of data to represent the multiple constraints in hydropower operation. This research is a pilot data-driven methodology for characterizing, classifying, and comparing the water-to-energy and energy-to-water signal transformations that hydropower facilities as signal processors accomplish. In this study, a Box Jenkins transfer function/noise model is used to identify the relationship between reservoir inflows and outflows. For examining the feasibility of this methodology, 5-minute fleet data for five storage and five run-of-river facilities was provided by the Tennessee Valley Authority (TVA) and transfer function models are developed. The influence of past inflow and outflow values on the current outflow decisions was investigated and summarized by examining the results of Box Jenkins methodology. Finally, dominance analysis was introduced to add value to the Box Jenkins model results and provide different stakeholders with a set of concepts to convey the functionality of hydropower

Quantitative Methods for Economics and Finance

This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice

Appropriate Wisdom, Technology, and Management toward Environmental Sustainability for Development

The protection and maintenance of environmental resources for future generations require responsible interaction between humans and the environment in order to avoid wasting natural resources. According to an ancient Native American proverb, “We do not inherit the Earth from our ancestors; we borrow it from our children.” This indigenous wisdom has the potential to play a significant role in defining environmental sustainability. Recent technological advances could sustain humankind and allow for comfortable living. However, not all of these advancements have the potential to protect the environment for future generations. Developing societies and maintaining the sustainability of the ecosystem require appropriate wisdom, technology, and management collaboration. This book is a collection of 19 important articles (15 research articles, 3 review papers, and 1 editorial) that were published in the Special Issue of the journal Sustainability entitled “Appropriate Wisdom, Technology, and Management toward Environmental Sustainability for Development” during 2021-2022.addresses the policymakers and decision-makers who are willing to develop societies that practice environmental sustainability, by collecting the most recent contributions on the appropriate wisdom, technology, and management regarding the different aspects of a community that can retain environmental sustainability

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

Principles and Applications of Data Science

Data science is an emerging multidisciplinary field which lies at the intersection of computer science, statistics, and mathematics, with different applications and related to data mining, deep learning, and big data. This Special Issue on “Principles and Applications of Data Science” focuses on the latest developments in the theories, techniques, and applications of data science. The topics include data cleansing, data mining, machine learning, deep learning, and the applications of medical and healthcare, as well as social media

Culture of Communication in The Space of Co-Working Newsrooom of Online Media

Technology has driven a change in the mainstream media editorial room towards the digital newsroom. Media that develops models of editorial space integrated with digital platforms has been widely practiced. Including, designing a newsroom work place to support the performance needed by media companies that are adaptive to change. The newsroom or editorial room no longer uses a cubical arrangement, but rather a shared work space. This research uses a constructionist paradigm according to a qualitative research approach with a phenomenological method. The results showed that the co-working space newsroom accelerated the coordination for the production of �breaking news�. Communication in the newsroom becomes without bureaucracy, consequently it becomes free of structure and a cross levels. The implication is that the newsroom culture of the co-working space becomes more flexible and fast in collaboration with fellow journalists and writers to raise the latest news issues. Another implication is that the newsroom supports the creative ideas of media actors

Examining the customer journey of solar home system users in Rwanda and forecasting their future electricity demand

Globally, 771 million people lack access to electricity, out of which 75% live in Sub-Saharan Africa (IEA, 2020b). Electricity grid expansion can be costly in rural areas, which often have low population densities. Solar home systems (SHS) have provided people worldwide an alternative option to gain electricity access. A SHS consists of a solar panel, battery and accompanying appliances. This research aims to advance the understanding of the SHS customer journey using a case study of SHS customers in Rwanda. This study developed a framework outlining households’ pre- to post-purchase experiences, which included awareness and purchase, both current and future SHS usage and finally customers’ upgrade, switching and retention preferences. A mixed methods approach was utilised to examine these steps, including structured interviews with the SHS providers’ customers (n=100) and staff (n=19), two focus groups with customers (n=24), as well as a time series analysis and descriptive statistics of database customers (n=63,299). A convolutional neural network (CNN) was created to forecast individual SHS users’ future electricity consumption in the next week, month and three months based on their previous hourly usage. Despite the volatility of SHS usage data, the CNN was able to forecast individual users’ future electricity more accurately than the naïve baseline, which assumes a continuation of previous usage. The time series analysis revealed an evening usage peak for non-television users, whilst customers with a television experienced an additional peak around midday. SHS recommendations prior and post-purchase were common, highlighting the circular nature of the customer journey. The main purchase reason and usage activity were having a clean energy source and phone charging respectively. A better understanding of the SHS customer journey may increase the number of households with electricity access, as companies can better address the purchase barriers and tap into the power of customer recommendations