River Basin Restoration and Management 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.

River Basin Restoration and Management 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.