9781843395102-184339510X-River Basin Restoration And Management

River Basin Restoration And Management

ISBN-13: 9781843395102
ISBN-10: 184339510X
Edition: 1
Author: J.M. Tyson, Avi Ostfeld
Publication date: 2005
Publisher: Iwa Pub
Format: Paperback 104 pages
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Book details

ISBN-13: 9781843395102
ISBN-10: 184339510X
Edition: 1
Author: J.M. Tyson, Avi Ostfeld
Publication date: 2005
Publisher: Iwa Pub
Format: Paperback 104 pages

Summary

River Basin Restoration And Management (ISBN-13: 9781843395102 and ISBN-10: 184339510X), written by authors J.M. Tyson, Avi Ostfeld, was published by Iwa Pub in 2005. With an overall rating of 4.1 stars, it's a notable title among other books. You can easily purchase or rent River Basin Restoration And Management (Paperback) from BooksRun, along with many other new and used books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

This book is the result of two workshops that took place at the 4th IWA World Water Congress: The Restoration of Degraded River Basins and River Basin Management Using Machine Learning. The Restoration of Degraded River Basins set out to share experience in the institutional, policy, and public participation elements of restoration programmes, the 'soft' issues surrounding restoration of a degraded river basin and the development of the river basin plan. The resulting papers include a number of case studies from a variety of river basins in Israel, South Africa, United Kingdom, Australia and Central Europe. The River Basin Management Using Machine Learning workshop highlighted and compared the two different approaches to watershed management: the physically based modelling approach relying on the system physics versus the data driven modelling approach based on exploring the system 'data behaviour'. The workshop was motivated by the recent rapid advance in information processing systems. These have pushed the hydrological research community to explore the possibilities of using intelligent systems aimed at automatically-evolving models of natural phenomena. This is the discipline of machine learning (ML), the study of computer algorithms that improve automatically through experience.

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