Flood Forecasting Using Machine Learning Methods

機構典藏 > College of Engineering > Graduate Institute & Department of Water Resources and Environmental Engineering > Monograph > Item 987654321/116247

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/116247

Title:	Flood Forecasting Using Machine Learning Methods
Authors:	Chang, Fi-John;Hsu, Kuolin;Chang, Li-Chiu
Date:	2019-02-28
Issue Date:	2019-04-16 12:10:19 (UTC+8)
Publisher:	Mdpi AG
Abstract:	Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.
Appears in Collections:	[Graduate Institute & Department of Water Resources and Environmental Engineering] Monograph

Files in This Item:

File	Size	Format
index.html	0Kb	HTML	353	View/Open

Loading...