Abstract
Achieving sustainable water resource management is crucial for creating resilient environmental systems. This research integrates intelligent systems into managing watershed ecosystems, focusing on the spatial and environmental design field. It addresses the complex interrelationships between river flow, water quality, and ecological health, aiming to develop spatially adaptive systems that contribute to Sustainable Development Goals (SDGs) through innovative, data-driven practices. Using advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), the project examines spatial dynamics within Taiwan’s watershed systems. By analyzing spatial patterns across ecological and hydrological systems, the goal is to inform sustainable design practices. The project has four key tasks: 1. Spatial Data Collection: Long-term data on streamflow, water quality, and ecosystem health will identify spatial and temporal variations for spatial mapping and visualization. 2. Topological Mapping and Modeling: Relationships between water quality, river flow, and ecosystems will be analyzed using deep-learning models, enhancing environmental design precision. 3. Spatiotemporal Design Models: Models predicting changes in water resources and ecosystems will improve spatial accuracy and guide sustainable watershed design. 4. Scenario-based Environmental Design: Comprehensive models will simulate environmental scenarios to design resilient spaces that tackle water resource management challenges. This research bridges advanced technological innovations with sustainable design, offering new methods for creating adaptive, resilient environmental spaces.
Presenters
Wen Ping TsaiAssistant Professor, Hydraulic and Ocean Engineering, National Cheng Kung University, Taiwan
Details
Presentation Type
Theme
Architectonic, Spatial, and Environmental Design
KEYWORDS
Resilient environments, Sustainable Development Goals, Artificial Intelligence, Sustainable design
