Miscellaneous

PREFACE The emergence of water, alongside energy and food, as one of the three major, interlinked, global environmental security issues provides abundant challenges and opportunities for the application of Machine Learning to such problems as optimization of water distribution and drainage networks’ design and operation, modelling and prediction of fluvial, pluvial, urban and coastal flooding, sediment transport and water quality issues. Advances in GIS, remote sensing and weather forecasting techniques mean that environmental data is becoming increasingly abundant at the same time as demands for solutions and tools to work on these problems become more urgent. Numerical models have been applied widely to improve the understanding and operational management of natural and manmade water systems. Traditionally, so-called “physicallybased” models have been applied for such purposes. However, such models are often computationally demanding, and frequently require significant data to constrain model structures and parameters. Data-Driven Models (DDMs) based on Machine Learning techniques - which seek to provide a mapping between the inputs and outputs of a given system, with little prior process knowledge – have emerged as an attractive option for prediction and classification in water systems. The principal benefit of such DDMs is their fast execution time, which allows many more model evaluations for a fixed computational budget. Such models have been applied widely to address a variety of problems within water systems modelling, including: system simulation (e.g. rainfall-runoff modelling/rating curve prediction) when trained on measured data, and also when employed as metamodels and trained to emulate models with a stronger physical (or process) basis; to improve the speed of the optimization procedure by acting as a surrogate model to the full fitness evaluation; to correct systematic errors in physically-based models during real-time forecasting; to provide uncertainty bound predictions during model forecasting when trained on uncertainty bounds derived from offline calibration; and in classification (for example of predicted severity of a hazard or exceedances of regulatory limits). Despite their potential benefit, successful application of machine learning techniques is not straightforward. A variety of machine learning techniques, optimization methods and evaluation procedures have been applied in the research literature. It is not always clear which methods will perform best in different settings, and how choices made will influence performance. As an example, different machine learning techniques might perform best depending on how their performance is evaluated within a given operational setting. Furthermore, although such methods are technically “black-box” models, system understanding may be required to choose the best input variables, and tailor the approach to the operational setting in question. With a view towards sharing the interdisciplinary knowledge required to make appropriate methodological decisions, papers are invited that explore issues of model design and application, and in particular, papers that compare different approaches for machine learning application.

Introduction  

• Any engineering design, i.e for chemical process plants/industries, is only useful when it can be translated into reality by using available materials and fabrication methods.
• Thus, the selection of materials for construction combined with the appropriate techniques of fabrication can play a vital role in the success or failure of a new chemical plant or in the improvement of an existing facility

INTRODUCTION TO THE STANDARD  ISO 9001 is one of the most widely adopted International Standards in the world. With over 1 million certificates issued worldwide, it spans all sectors of commerce and industry. ISO 9000 was first published by ISO in 1987 having evolved from the BS 5750 series of standards. These standards themselves had been influenced by Government and military procurement standards which had become necessary to ensure the quality of products and services purchased and distributed 

Introduction Business risk around water is rapidly entering the boardroom and risk managers’ realm. A few high profile cases1 have alerted business leaders to these risks and some companies are responding by seeking to put in place systems for understanding and addressing water-related risks to their operations, supply chains and brands. The increasing public and corporate awareness of climate change over the past decade has focused broad attention on water as a key resource under threat. A 2008 Goldman Sachs ‘Top Five Risks’ conference identified a catastrophic global water shortage as a greater global risk than soaring food prices and exhaustion of energy reserves during the 21st Century. Most businesses will find it difficult to manage all of their water risks alone. Given the complexity of the issues and the political and social importance of water, engagement with civil society, other companies and the public sector is necessary. This report briefly outlines the nature of the global corporate risk around water and highlights ways in which business can better manage this growing risk. The report focuses on water scarcity as the major global issue affecting business, but similar issues often arise where water quality presents risks to companies