Design Hydrology and Sedimentology for Small Catchments
Design Hydrology and Sedimentology for Small Catchments

Author: C. T. Haan

Publisher: Academic Press

Published: 1994-08-17

Total Pages: 603

ISBN-13: 0080571646

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The Clean Water Act, with its emphasis on storm water and sediment control in urban areas, has created a compelling need for information in small-catchment hydrology. Design Hydrology and Sedimentology for Small Catchments provides the basic information and techniques required for understanding and implementing design systems to control runoff, erosion, and sedimentation. It will be especially useful to those involved in urban and industrial planning anddevelopment, surface mining activities, storm water management, sediment control, and environmental management.This class-tested text, which presents many solved problems throughout as well as solutions at the end of each chapter, is suitable for undergraduate, graduate, and continuing education courses. In addition, practicing professionals will find it a valuable reference.Anderson/Woessner: APPLIED GROUNDWATER MODELING (1992)Shuirman/Slosson: FORENSIC ENGINEERING (1992)de Marsily: QUANTITATIVE HYDROGEOLOGY (1986)Selley: APPLIED SEDIMENTOLOGY, THIRD EDITION (1988)Huyakorn: COMPUTATIONAL METHODS IN SUBSURFACE FLOW (1986)Pinder: FINITE ELEMENT MODELING IN SURFACE AND SUBSURFACE HYDROLOGY (1977)Key Features* Covers major new improvements and state-of-the-art technologies in sediment control technology* Provides in-depth information on estimating the impact of land-use changes on runoff and flood flows, as well as on estimating erosion and sediment yield from small catchments* Presents superior coverage on design of flood and sediment detention ponds and design of runoff and sediment control measures

Flood Forecasting Using Machine Learning Methods
Flood Forecasting Using Machine Learning Methods

Author: Fi-John Chang

Publisher: MDPI

Published: 2019-02-28

Total Pages: 376

ISBN-13: 3038975486

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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.