Dr. Chen-Wei Chen, Associate Professor

Department of Marine Structures, Ship Engineering and Ocean Engineering, Ocean College, Zhejiang University, Hangzhou, China

Biography

Dr. Chen-Wei Chen was born in 1980 in Taipei, Taiwan of China. Currently, he is an Associate Professor of Department of Naval Architecture and Ocean Engineering, Ocean College, Zhejiang University, Zhoushan, P.R. China, since 2013. He received the Ph.D. degree at the Department of Engineering Science and Ocean Engineering, National Taiwan University (NTU), Taipei, Taiwan in 2013 and received the marine engineering degree in naval architecture and marine engineering from the National Cheng Kung University (NCKU), Tainan of Taiwan. His research interests include Hydrodynamics, System Dynamics, Maneuvering and Seakeeping performance of marine vehicle (ship and submarine) and ocean platform, etc. There is in-depth research in these research fields including design and development of special propeller propulsion system, active wave compensation system, novel submarine vehicle and ocean platform motion control and satellite positioning monitoring measurement technology for ships, etc. In the past year, he serves as a Reviewer for peer-reviewed journals, including Ocean Engineering, IEEE Journal of Oceanic Engineering, Journal of Navigation, Journal of Sensors, Journal of Marine Science and Technology, Journal of Marine Science and Application, Journal of Marine Science and Engineering and Journal of Applied Sciences, etc. Simultaneously invited to serve as a special issue editor of Applied Sciences in Special Issue title of Computational Fluid Dynamics-based for Ship Hydrodynamics Applications. In recent years, he has published over 30 high-level related SCI and/or EI research papers.

Topic

Research and Application of High-Performance Energy-Saving Kappel Propeller for Ship and Underwater Vehicle

Abstract

The study introduced the state-of-art of marine propulsion system and Kappel propeller; Concept Design for a high-performance energy-saving Kappel propeller; Application of Kappel propeller and CRP on Marine Ships and Underwater Vehicle. CFD study of energy-saving enhanced performance for Kappel propellers on hydrodynamics, cavitation and noises, etc.

Dr. Norhidayu Kasim, Associate Professor

Department of Civil Engineering, Faculty of Engineering, Islamic University of Malaysia, Gombak, Malaysia

Biography

Dr. Norhidayu Kasim is an associate professor, practised engineer as well as researcher with a deep focus on geotechnical engineering, specializing in rainfall-induced landslides and early warning systems. With extensive experience in analyzing geological and environmental hazards, she has contributed significantly to the development of effective prediction models for landslides, particularly in regions prone to extreme weather events. Her expertise spans across multiple disciplines, including civil engineering, geology, and climate science, where she integrates advanced computational tools to tackle complex environmental challenges. She has been actively involved in various research projects to improve disaster preparedness and reduce the impact of natural hazards on communities. She has a solid background in employing Artificial Neural Networks (ANNs) to enhance the precision of forecasting models, which have proven instrumental in identifying critical rainfall patterns linked to landslide occurrences. Her research combines empirical data with cutting-edge technologies, leading to the creation of adaptable rainfall thresholds applicable in diverse geological settings.

Topic

Adapting Rainfall Thresholds for Landslide Prediction: A Comprehensive Approach for Early Warning Systems

Abstract

Rainfall-induced landslides are a critical challenge faced by many regions worldwide, posing severe threats to human life, infrastructure, and the environment. As extreme weather events become more frequent due to climate change, there is an urgent need for more effective methods to predict and mitigate the impacts of landslides. Universal rainfall thresholds should be developed and adapted in various climates and geological settings to forecast landslides and support the establishment of early warning systems. Through an analysis of landslide-triggering rainfall data from multiple case studies, this research proposes two essential thresholds which are the Intensity-Duration threshold (I-D) and Cumulative-Duration threshold (C-D). These thresholds serve as predictive tools by identifying critical rainfall patterns often preceding landslide occurrences. To enhance their accuracy, advanced computational techniques such as Artificial Neural Networks (ANNs) are employed, allowing for the modelling of complex relationships between rainfall intensity, duration, and the likelihood of landslides. In addition, the study examines how key factors such as soil characteristics, rock type, and slope dynamics contribute to regional variations in landslide susceptibility. By integrating these factors into rainfall threshold models, it becomes possible to develop highly localized early warning systems capable of providing timely alerts and reducing the risk of disaster. This will explore how these global thresholds can be adapted to different regions and environments, offering a critical tool for governments and disaster management agencies. The aim is to enhance preparedness, reduce casualties, and safeguard communities from the devastating effects of rainfall-induced landslides.