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Why Arc hydro? / David Maidment / - Arc Hydro framwork / David Maidment, Scott Morehouse / - Hydro networks / Francisco Olivera, David Maidment / - Drainage systems / Francisco Olivera, Jordan Furnans / River channels / Nawajish Noma, James Nelson / Hydrography / Kim Davis, Jordan Furnans / - Time series / Damid Maidment, Venkatesh Merwade / - Hydrologic modeling / Steve Grise, David Arctur.
One of the most powerful, yet relatively unknown features available in HEC-RAS is the HECRASController. TheHECRASController API has a wealth of procedures which allow a programmer to manipulate HEC-RAS externally by setting input data, retrieving input or output data, and performing common functions such as opening and closing HEC-RAS, changing plans, running HEC-RAS, and plotting output. HECRASController applications are seemingly endless. Not only can the retrieval and post-processing of output be automated, but with the HECRASController, real-time modeling and probabilistic experiments like Monte Carlo are possible. If you have HEC-RAS on your computer, you already have the HECRASController! "Breaking the HEC-RAS Code" explains how the HECRASController works, provides example applications of the HECRASController, and catalogs the vast array of programming procedures (with explanations and examples on how to use them) embedded in the HECRASController. This is a "must-have" book for all HEC-RAS users. Professionals: Give yourself an edge for the next proposal and do something groundbreaking with HEC-RAS. Students: Make yourself marketable by adding the skills offered in this book.
A comprehensive guide to managing and mitigating natural disasters Recent years have seen a surge in the number, frequency, and severity of natural disasters, with further increases expected as the climate continues to change. However, advanced computational and geospatial technologies have enabled the development of sophisticated early warning systems and techniques to predict, manage, and mitigate disasters.Techniques for Disaster Risk Management and Mitigation explores different approaches to forecasting disasters and provides guidance on mitigation and adaptation strategies. Volume highlights include: Review of current and emerging technologies for disaster prediction Different approaches to risk management and mitigation Strategies for implementing disaster plans and infrastructure improvements Guidance on integrating artificial intelligence with GIS and earth observation data Examination of the regional and global impacts of disasters under climate variability
Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources.The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies.The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences.
R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The basic notion of artificial neural networks (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an artificial neuron. Research in this field remained somewhat dormant in the early years, perhaps because of the limited capabilities of this method and because there was no clear indication of its potential uses. However, interest in this area picked up momentum in a dramatic fashion with the works of Hopfield (1982) and Rumelhart et al. (1986). Not only did these studies place artificial neural networks on a firmer mathematical footing, but also opened the dOOf to a host of potential applications for this computational tool. Consequently, neural network computing has progressed rapidly along all fronts: theoretical development of different learning algorithms, computing capabilities, and applications to diverse areas from neurophysiology to the stock market. . Initial studies on artificial neural networks were prompted by adesire to have computers mimic human learning. As a result, the jargon associated with the technical literature on this subject is replete with expressions such as excitation and inhibition of neurons, strength of synaptic connections, learning rates, training, and network experience. ANNs have also been referred to as neurocomputers by people who want to preserve this analogy.