Decision Support Methods for Assessing Flood Risk and Vulnerability, Ahmed Karmaoui
Название: Flood Forecasting Using Machine Learning Methods ISBN: 3038975486 ISBN-13(EAN): 9783038975489 Издательство: Неизвестно Рейтинг: Цена: 13169.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
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.
Описание: This book provides an information fusion model with information fusion theory, geographic information system technology and modern mathematical methods to evaluate the risks of groundwater inrushes from aquifers underlying coal seams. In this new model, the water inrush vulnerable index was calculated with variable weights theory. It overcomes the defect of the traditional vulnerability index method that assumes constant weights for the factors controlling the water inrush. Mine water inrush events often occur during coal mine construction and production; they account for a large proportion of the nation’s coal mine disasters and accidents in China. Between 2005 and 2014, 513 water inrush incidents have occurred with a total loss of 2,753 lives. As mining depths and mining intensity continue to increase, the hydrogeological conditions encountered are becoming more complex. The innovative model presented here was applied to two coal mines in China with proved better results than the traditional vulnerability index method.
Описание: This book provides an information fusion model with information fusion theory, geographic information system technology and modern mathematical methods to evaluate the risks of groundwater inrushes from aquifers underlying coal seams.
Описание: The book provides also a review of weighting methods applied in various multi-criteria decision-making (MCDM) methods and also presents survey results on priority ranking of watershed management criteria undertaken by 30 undergraduate and postgraduate students from the Faculty of Civil Engineering, Universiti Teknologi Malaysia.
Автор: Bruce L. Rhoads Название: River Dynamics: Geomorphology to Support Management ISBN: 110719542X ISBN-13(EAN): 9781107195424 Издательство: Cambridge Academ Рейтинг: Цена: 10613.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A comprehensive overview of the geomorphological processes that shape rivers, combining process-based theoretical and geohistorical approaches. It discusses how the dynamics of rivers are being affected by human activity, and how river management techniques which work together with the dynamics of rivers can best be used to restore these systems.
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