Implementation and Evaluation of Artificial Neural Networks for River Flood Prediction PDF Download
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Author: Mohammad Taghi Dastorani
Publisher:
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Category :
Languages : en
Pages :
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Author: Mohammad Taghi Dastorani
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Category :
Languages : en
Pages :
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Author: Fi-John Chang
Publisher: MDPI
ISBN: 3038975486
Category : Technology & Engineering
Languages : en
Pages : 376
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Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.
Author: P. Varoonchotikul
Publisher: CRC Press
ISBN: 9781138475076
Category :
Languages : en
Pages :
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This dissertation considers various questions with respect to the effects of salinity on nutrification: what are the main inhibiting factors causing the effects, do all salts have similar effects, what is the maximum acceptable salt level, are ammonia oxidisers or nitrite oxidizers most sensitive to salt stress, can nitrifiers adapt to long term salt stress and are some specific nitrifiers more resistant to salt stress than others? Research was carried out at laboratory scale and in full-scale plants and modelling was employed in both phases to provide a mathematical description for salt inhibition on nitrification and to facilitate the comparison. The result has led to an improved understanding of the effect of salinity on nitrification. The results can be used to improve the sustainability of the exisisting wastewater treatment plants operated under salt stress.
Author: P Varoonchotikul
Publisher: CRC Press
ISBN: 9789058096319
Category : Technology & Engineering
Languages : en
Pages : 102
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Book Description
Flood disasters continue to occur in many countries in the world and cause tremendous casualties and property damage. To mitigate the effects of floods, a range of structural and non-structural measures have been employed including dykes, channelling, flood-proofing property, land-use regulation and flood warning schemes. Such schemes can include the use of Artificial Neural Networks (ANN) for modelling the rainfall run-off process as it is a quick and flexible approach which gives very promising results. However, the inability of ANN to extrapolate beyond the limits of the training range is a serious limitation of the method, and this book examines ways of side-stepping or solving this complex issue.
Author: R.S. Govindaraju
Publisher: Springer Science & Business Media
ISBN: 9401593418
Category : Science
Languages : en
Pages : 338
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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.
Author: P. V. Timbadiya
Publisher: Springer Nature
ISBN: 9819919010
Category : Science
Languages : en
Pages : 605
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Book Description
This book comprises the proceedings of the 26th International Conference on Hydraulics, Water Resources and Coastal Engineering (HYDRO 2021) focusing on broad spectrum of emerging opportunities and challenges in the field of soft computing and geospatial techniques in water resources engineering. It covers a range of topics, including, but not limited to, satellite derived data for hydrologic applications, GIS and RS applications in water resources management, rainfall and streamflow prediction, hydro-informatics, data driven and artificial intelligent based hydrological modelling, optimization of water resources systems, etc. Presenting recent advances in the form of illustrations, tables, and text, it offers readers insights for their own research. In addition, the book addresses fundamental concepts and studies in the field of Soft Computing and Geospatial Techniques in Water Resources Engineering, making it a valuable resource for both beginners and researchers wanting to further their understanding of hydraulics, water resources and coastal engineering.
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 90
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Author: Kevin Sene
Publisher: Springer
ISBN: 331923546X
Category : Science
Languages : en
Pages : 427
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Book Description
This second edition explores some of the latest techniques used to provide forecasts for a wide range of water-related applications in areas such as floods, droughts, water resources and environmental impacts. The practical uses can range from decisions on whether to issue a flood warning through to providing longer-term advice such as on when to plant and harvest crops or how to operate reservoirs for water supply and hydropower schemes. It provides an introduction to the topic for practitioners and researchers and useful background for courses in areas such as civil engineering, water resources, meteorology and hydrology. As in the first edition, the first section considers topics such as monitoring and forecasting techniques, demand forecasting and how forecasts are interpreted when issuing warnings or advice. Separate chapters are now included for meteorological and catchment monitoring techniques allowing a more in-depth discussion of topics such as weather radar and water quality observations. The chapters on meteorological and hydrological forecasting now include a greater emphasis on rainfall forecasting and ensemble and probabilistic techniques. Regarding the interpretation of forecasts, an updated chapter discusses topics such as approaches to issuing warnings and the use of decision support systems and risk-based techniques. Given the rapid pace of development in flash flood fore casting techniques, flash floods and slower responding riverine floods are now considered in separate chapters. This includes more detail on forecasting floods in large river basins and on methods for providing early warnings of debris flows, surface water flooding and ice jam and dam break floods. Later chapters now include more information on developing areas such as environmental modelling and seasonal flow forecasting. As before examples of operational systems are provided throughout and the extensive sets of references which were a feature of the first edition have been revised and updated. Key themes • floods • droughts • meteorological observations • catchment monitoring • meteorological forecasts • hydrological forecasts • demand forecasts • reservoirs • water resources • water quality • decision support • data assimilation • probabilistic forecasts Kevin Sene is a civil engineer and researcher with wide experience in flood risk management, water resources and hydrometeorology. He has previously published books on flood warning, forecasting and emergency response and flash floods (Springer 2008, 2013).
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 348
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Author: Srinivasa Lingireddy
Publisher: ASCE Publications
ISBN: 9780784475607
Category : Technology & Engineering
Languages : en
Pages : 196
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Book Description
Prepared by the Water Supply Engineering Technical Committee of the Infrastructure Council of the Environmental and Water Resources Institute of ASCE. This report examines the application of artificial neural network (ANN) technology to water supply engineering problems. Although ANN has rarely been used in in this area, those who have done so report findings that were beyond the capability of traditional statistical and mathematical modeling tools. This report describes the availability of diverse applications, along with the basics of neural network modeling, and summarizes the experiences of groups of researchers around the world who successfully demonstrated significant benefits from using ANN technology in water supply engineering. Topics include: Forecasting salinity levels in River Murray, South Australia; Predicting gastroenteritis rates and waterborne outbreaks; Modeling pH levels in a eutrophic Middle Loire River, France; and ANNs as function approximation tools replacing rigorous mathematical simulation models for analyzing water distribution networks.
