Flood Forecasting Using Machine Learning Methods

Book Description

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 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 for flood hazard mitigation. Emerging advances in technologies coupled with big-data mining have boosted data-driven applications, among which , with its flexibility and scalability in pattern extraction, has modernized not only 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.

Book Details

  • Title: Flood Forecasting Using Machine Learning Methods
  • Author: , ,
  • Length: 376 pages
  • Edition: 1
  • Language: English
  • Publisher:
  • Publication Date: 2019-02-28
  • ISBN-10: 3038975486
  • ISBN-13: 9783038975489
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