An Intelligent Urban Flood Control Decision Support System: A Case Study of Taipei City, Taiwan
Dr. Chang is a prominent leader in AI and big-data analytics, specializing in transdisciplinary research across hydroinformatics, eco-environments, and agricultural engineering and publishing over 180 SCI papers (h-index=53). In particular, he and his team have pioneered the development of AI and big-data analytics techniques for flood forecasting and emergency management, tailored to address the long-standing challenges related to flood risk management. He has extensive experience in leading international collaboration projects and has received many prestigious awards recognizing his profound contribution to hydroinformatics technology development and intelligent flood risk management practice. He has long served as associate editor for Journal of Hydrology and Hydrological Sciences Journal and as Taiwan鈥檚 national correspondent for the IAHS. He is the Founding President of the 鈥淭aiwan Hydro-Informatics Society鈥� and the 鈥淎gricultural Carbon Management Association鈥�.
Speech Title: An Intelligent Urban Flood Control Decision Support System: A Case Study of Taipei City, Taiwan
Abstract
Floods are a frequent threat in Taiwan, and we aim to enhance disaster prevention by developing an intelligent urban flood control decision support system using hydroinformatics technology. Leveraging years of experience in AI for hydrology and the environment, we explore novel AI techniques, including data mining, DL, and meta-heuristics, integrated with big data and IoT monitoring, to enhance flood forecasting accuracy and intelligent pump operation strategies. Using Taipei City as a case study, we aim to 1) build a real-time inundation forecasting database, 2) estimate flood extent and depth based on IoT water level and flood monitoring, and 3) construct advanced DL-based sequence-to-sequence and multi-horizon, multi-input-output models for high-resolution urban flood forecasting. A key innovation is integrating inundation forecasts and sewer water level data to optimize pump operation strategies, coupling IoT sensing with edge computing to reduce data transmission and enhance security. Ultimately, this system will improve disaster preparedness by enabling intelligent early warnings and disaster prevention management.
Contact and booking details
- Email address
- unesco.imrr@mailbox.lboro.ac.uk
- Booking information
- Register online for this webinar