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Given the nonlinearity, complexity and limited physical understanding of many processes occurring within water resource systems, artificial neural networks may at times be the best available tool for modelling such systems. However, despite the increasing use of neural network models, they are still viewed with some scepticism by users of more conventional modelling methodologies, primarily due to their black box nature. In this book, a new Bayesian framework for developing artificial neural networks is presented, which aims to address what are considered to be the three most significant issues hindering the wider acceptance of artificial neural networks in the field of water resources engineering; namely generalisability, interpretability and uncertainty. Throughout the development of this framework, emphasis is placed on obtaining accurate results, while maintaining simplicity of implementation, which is considered to be of utmost importance for adoption of the framework by practitioners in the field of water resources engineering.

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Martin Lambert is a professor of psychiatry at the University of Hamburg, where he is the head of the Psychosis Center, which includes the Psychosis Early Detection and Intervention Center (PEDIC). He performed his psychiatric training at the Department of Psychiatry and Psychotherapy at the University Medical Center in Hamburg, Germany. During his training, he spent 2 years at the Early Psychosis Prevention and Intervention Centre (EPPIC) in Melbourne, Australia. Professor Lambert´s current research interests include the pharmacological and psychosocial treatment of schizophrenic, bipolar, and first-episode psychosis patients, and, especially, aspects of integrated care, remission and recovery, quality of life, and subjective wellbeing. Professor Lambert is the head of the network for a better mental health in Hamburg, which was founded by the German Research Association. He is the editor and author of several books about schizophrenia and has published various articles on schizophrenia and first-episode psychosis.

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