TY - THES A1 - Acharya, Bhuwan T1 - VISUALIZATION OF FLOOD SCENARIOS IN GERMANY IN 2021 AND FORECASTING FLOOD USING MACHINE-LEARNING MODELS N2 - Floods are the most common, devastating, and frequently occurring natural disaster nowadays. Because of climate change, Europe is expected to see an even higher number of floods in the coming decade. As floods cannot be prevented, understanding the pattern and causes and being able to forecast could significantly reduce the losses during the flood. In July 2021, Germany faced a catastrophic flood, taking more than 180 people’s lives and causing around 40 billion euros of economic loss. To study the flooded region and the region at high risk, the Flood event 2021 has been visualized in this research. The Flood map has been created using a sentinel-1 image. In the visualization, floods in the Rhine River and Arh River can be seen, and the change in the pattern of flood from 12th July to 16th July has also been observed. Different models exist that try to predict floods and warn as early as possible. Since Machine learning is popular nowadays in every sector, ML models have been implemented in this study to forecast the flood. The SARIMA, Random Forest, and LSTM have been implemented using historical data to predict the flood. The Random Forest model performed better than the remaining two models. The Isolation Forest model has also been implemented to classify the data into Flood and No Flood. This model performs well with the Flood classes but struggles in capturing the No Flood class. KW - Flood KW - Machine learning KW - sentinel KW - visualization Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-18780 ER - TY - THES A1 - Oha, Kingsley T1 - ADVANCEMENTS IN MICROSERVICE ARCHITECTURES: TACKLING DATA COMMUNICATION, SCALABILITY, AND CI/CD AUTOMATION CHALLENGES N2 - This paper delves into the optimization of microservice architectures, a critical aspect of contemporary software engineering, by focusing on three pivotal areas: efficient data communication, scalable design, and the automation of Continuous Integration/Continuous Deployment (CI/CD) processes. The study begins by examining the intricacies of data communication within microservices, identifying challenges such as network latency, data consistency, and fault tolerance. Strategies for efficient data handling, including advanced message queuing techniques and API gateways, are explored. The paper then shifts its focus to scalable design, assessing how microservices can be architected to gracefully handle varying loads and facilitate seamless scaling. This includes an analysis of containerization technologies, service orchestration, and load balancing strategies. The final part of the study addresses the automation of CI/CD processes, a cornerstone for achieving agility and high velocity in microservices development and deployment. The evaluation of modern CI/CD tools and methodologies, highlighting their role in enhancing deployment frequency, reducing lead time for changes, and improving overall system reliability. This comprehensive study aims to provide valuable insights and practical guidelines for software professionals and researchers, contributing to the refinement and advancement of microservice architectures in the dynamic world of software development. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-20463 ER -