数据加载中.....
|
jsp.display-item.identifier=請使用永久網址來引用或連結此文件:
https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/116247
|
| 题名: | Flood Forecasting Using Machine Learning Methods |
| 作者: | Chang, Fi-John;Hsu, Kuolin;Chang, Li-Chiu |
| 日期: | 2019-02-28 |
| 上传时间: | 2019-04-16 12:10:19 (UTC+8) |
| 出版者: | Mdpi AG |
| 摘要: | 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. |
| 显示于类别: | [水資源及環境工程學系暨研究所] 專書
|
文件中的档案:
| 档案 |
大小 | 格式 | 浏览次数 |
|---|
| index.html | 0Kb | HTML | 450 | 检视/开启 |
|
在機構典藏中所有的数据项都受到原著作权保护.
|
