Neural Networks For Hydrological Modeling

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Neural Networks For Hydrological Modeling

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Author : Robert Abrahart
language : en
Publisher: CRC Press
Release Date : 2004-05-15

Neural Networks For Hydrological Modeling written by Robert Abrahart and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-05-15 with Science categories.

A new approach to the fast-developing world of neural hydrological modelling, this book is essential reading for academics and researchers in the fields of water sciences, civil engineering, hydrology and physical geography. Each chapter has been written by one or more eminent experts working in various fields of hydrological modelling. The b

Artificial Neural Networks In Hydrology

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Author : R.S. Govindaraju
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09

Artificial Neural Networks In Hydrology written by R.S. Govindaraju and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Science categories.

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.

Neural Networks For Hydrological Modeling

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Author : Robert Abrahart
language : en
Publisher: CRC Press
Release Date : 2004-05-15

Neural Networks For Hydrological Modeling written by Robert Abrahart and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-05-15 with Science categories.

A new approach to the fast-developing world of neural hydrological modelling, this book is essential reading for academics and researchers in the fields of water sciences, civil engineering, hydrology and physical geography. Each chapter has been written by one or more eminent experts working in various fields of hydrological modelling. The b

Artificial Intelligence And Soft Computing Icaisc 2004

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Author : Leszek Rutkowski
language : en
Publisher: Springer Science & Business Media
Release Date : 2004-06-01

Artificial Intelligence And Soft Computing Icaisc 2004 written by Leszek Rutkowski and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-06-01 with Computers categories.

This book constitutes the refereed proceedings of the 7th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2004, held in Zakopane, Poland in June 2004. The 172 revised contributed papers presented together with 17 invited papers were carefully reviewed and selected from 250 submissions. The papers are organized in topical sections on neural networks, fuzzy systems, evolutionary algorithms, rough sets, soft computing in classification, image processing, robotics, multiagent systems, problems in AI, intelligent control, modeling and system identification, medical applications, mechanical applications, and applications in various fields.

Advances In Data Based Approaches For Hydrologic Modeling And Forecasting

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Author : Bellie Sivakumar
language : en
Publisher: World Scientific
Release Date : 2010

Advances In Data Based Approaches For Hydrologic Modeling And Forecasting written by Bellie Sivakumar and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Science categories.

This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.

Artificial Intelligence In Iot

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Author : Fadi Al-Turjman
language : en
Publisher: Springer
Release Date : 2019-02-12

Artificial Intelligence In Iot written by Fadi Al-Turjman and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-12 with Technology & Engineering categories.

This book provides an insight into IoT intelligence in terms of applications and algorithmic challenges. The book is dedicated to addressing the major challenges in realizing the artificial intelligence in IoT-based applications including challenges that vary from cost and energy efficiency to availability to service quality in multidisciplinary fashion. The aim of this book is hence to focus on both the algorithmic and practical parts of the artificial intelligence approaches in IoT applications that are enabled and supported by wireless sensor networks and cellular networks. Targeted readers are from varying disciplines who are interested in implementing the smart planet/environments vision via intelligent wireless/wired enabling technologies. Includes the most up-to-date research and applications related to IoT artificial intelligence (AI); Provides new and innovative operational ideas regarding the IoT artificial intelligence that help advance the telecommunications industry; Presents AI challenges facing the IoT scientists and provides potential ways to solve them in critical daily life issues.

Hydrological Data Driven Modelling

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Author : Renji Remesan
language : en
Publisher: Springer
Release Date : 2014-11-03

Hydrological Data Driven Modelling written by Renji Remesan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-03 with Science categories.

This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.

Broadening The Use Of Machine Learning In Hydrology

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Author : Chaopeng Shen
language : en
Publisher: Frontiers Media SA
Release Date : 2021-07-08

Broadening The Use Of Machine Learning In Hydrology written by Chaopeng Shen and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-08 with Science categories.

Deep Learning For Hydrometeorology And Environmental Science

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Author : Taesam Lee
language : en
Publisher: Springer Nature
Release Date : 2021-01-27

Deep Learning For Hydrometeorology And Environmental Science written by Taesam Lee and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-27 with Science categories.

This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.