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Artificial Neural Networks in Hydrology

Artificial Neural Networks in Hydrology PDF Author: R.S. Govindaraju
Publisher: Springer Science & Business Media
ISBN: 9401593418
Category : Science
Languages : en
Pages : 338

Book Description
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.

Artificial Neural Networks in Hydrology

Artificial Neural Networks in Hydrology PDF Author: R.S. Govindaraju
Publisher: Springer Science & Business Media
ISBN: 9401593418
Category : Science
Languages : en
Pages : 338

Book Description
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.

State of the World's Forests 2007

State of the World's Forests 2007 PDF Author: Food and Agriculture Organization of the United Nations
Publisher: Food & Agriculture Org.
ISBN: 9789251055861
Category : Business & Economics
Languages : en
Pages : 160

Book Description
This is the 7th edition of the FAO's biennial report on the state of global forest resources, recent developments and emerging issues in the forest sector. This edition focuses on progress towards sustainable forest management at the regional and global levels, with each regional report structured according to seven thematic elements: the extent of forest resources; biological diversity; forest health and vitality; productive functions of forest resources; protective functions of forest resources; socio-economic functions; and legal, policy and institutional frameworks. These summaries are based on the most current information available, including new data from the Global Forest Resources Assessment 2005 (FRA 2005). The report goes on to consider 18 key issues in the forest sector, including: climate change, forest landscape restoration, forest tenure, invasive species, wildlife management and wood energy. The overall conclusion is that whilst progress is being made, it is very uneven with those regions with developing economies and tropical ecosystems continuing to lose forest area whilst lacking adequate institutions to reverse this trend. The biggest limitation for evaluating progress is weak data, with many countries lacking the financial resources to undertake national forest assessments.

Advances in Streamflow Forecasting

Advances in Streamflow Forecasting PDF Author: Priyanka Sharma
Publisher: Elsevier
ISBN: 0128209240
Category : Science
Languages : en
Pages : 406

Book Description
Advances in Streamflow Forecasting: From Traditional to Modern Approaches covers the three major data-driven approaches of streamflow forecasting including traditional approach of statistical and stochastic time-series modelling with their recent developments, stand-alone data-driven approach such as artificial intelligence techniques, and modern hybridized approach where data-driven models are combined with preprocessing methods to improve the forecast accuracy of streamflows and to reduce the forecast uncertainties. This book starts by providing the background information, overview, and advances made in streamflow forecasting. The overview portrays the progress made in the field of streamflow forecasting over the decades. Thereafter, chapters describe theoretical methodology of the different data-driven tools and techniques used for streamflow forecasting along with case studies from different parts of the world. Each chapter provides a flowchart explaining step-by-step methodology followed in applying the data-driven approach in streamflow forecasting. This book addresses challenges in forecasting streamflows by abridging the gaps between theory and practice through amalgamation of theoretical descriptions of the data-driven techniques and systematic demonstration of procedures used in applying the techniques. Language of this book is kept simple to make the readers understand easily about different techniques and make them capable enough to straightforward replicate the approach in other areas of their interest. This book will be vital for hydrologists when optimizing the water resources system, and to mitigate the impact of destructive natural disasters such as floods and droughts by implementing long-term planning (structural and nonstructural measures), and short-term emergency warning. Moreover, this book will guide the readers in choosing an appropriate technique for streamflow forecasting depending upon the given set of conditions. - Contributions from renowned researchers/experts of the subject from all over the world to provide the most authoritative outlook on streamflow forecasting - Provides an excellent overview and advances made in streamflow forecasting over the past more than five decades and covers both traditional and modern data-driven approaches in streamflow forecasting - Includes case studies along with detailed flowcharts demonstrating a systematic application of different data-driven models in streamflow forecasting, which helps understand the step-by-step procedures

Flood Forecasting Using Machine Learning Methods

Flood Forecasting Using Machine Learning Methods PDF Author: Fi-John Chang
Publisher: MDPI
ISBN: 3038975486
Category : Technology & Engineering
Languages : en
Pages : 376

Book Description
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.

Risk in Water Resources Management

Risk in Water Resources Management PDF Author: International Association of Hydrological Sciences
Publisher: International Association of Hydrological Sciences
ISBN: 9781907161223
Category : Risk managment
Languages : en
Pages : 0

Book Description
Proceedings of the Symposium HS03 held dring th eUIGG GA in Melbourne (28 June - 7 July 2011)

Hydrological Modelling and the Water Cycle

Hydrological Modelling and the Water Cycle PDF Author: Soroosh Sorooshian
Publisher: Springer Science & Business Media
ISBN: 3540778438
Category : Science
Languages : en
Pages : 294

Book Description
This volume is a collection of a selected number of articles based on presentations at the 2005 L’Aquila (Italy) Summer School on the topic of “Hydrologic Modeling and Water Cycle: Coupling of the Atmosphere and Hydrological Models”. The p- mary focus of this volume is on hydrologic modeling and their data requirements, especially precipitation. As the eld of hydrologic modeling is experiencing rapid development and transition to application of distributed models, many challenges including overcoming the requirements of compatible observations of inputs and outputs must be addressed. A number of papers address the recent advances in the State-of-the-art distributed precipitation estimation from satellites. A number of articles address the issues related to the data merging and use of geo-statistical techniques for addressing data limitations at spatial resolutions to capture the h- erogeneity of physical processes. The participants at the School came from diverse backgrounds and the level of - terest and active involvement in the discussions clearly demonstrated the importance the scienti c community places on challenges related to the coupling of atmospheric and hydrologic models. Along with my colleagues Dr. Erika Coppola and Dr. Kuolin Hsu, co-directors of the School, we greatly appreciate the invited lectures and all the participants. The members of the local organizing committee, Drs Barbara Tomassetti; Marco Verdecchia and Guido Visconti were instrumental in the success of the school and their contributions, both scienti cally and organizationally are much appreciated.

Flood Forecasting Using Artificial Neural Networks

Flood Forecasting Using Artificial Neural Networks PDF Author: P Varoonchotikul
Publisher: CRC Press
ISBN: 9789058096319
Category : Technology & Engineering
Languages : en
Pages : 102

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.

Hydrology of the Black Hills Area, South Dakota

Hydrology of the Black Hills Area, South Dakota PDF Author:
Publisher:
ISBN:
Category : Black Hills (S.D. and Wyo.)
Languages : en
Pages : 164

Book Description


Hydrology

Hydrology PDF Author: Martin P. Wanielista
Publisher: John Wiley & Sons
ISBN: 0471072591
Category : Science
Languages : en
Pages : 598

Book Description
This edition of its popular predecessor has been significantly revised to increase flexibility in the presentation and maintain greater continuity of the material. Combining both theory and practical applications of empirical equations the text contains expanded treatment of water quantity and quality control, a detailed presentation of basic principles and use in analysis and design, hydrograph topics including synthetic and convolution techniques, practical and realistic case studies relating to design problems, and additional end-of-chapter problems. It provides new computer programs to explain complex concepts and solve large data-based problems. An additional appendix offers suggestions for classroom or lab problems.

An Introduction to Time Series Analysis and Forecasting

An Introduction to Time Series Analysis and Forecasting PDF Author: Robert A. Yaffee
Publisher: Academic Press
ISBN: 0127678700
Category : Mathematics
Languages : en
Pages : 556

Book Description
A time series is a set of repeated measurements of the same phenomenon taken sequentially over time. Capturing the data creates a time series "memory" to document correlations or lack, and to help them make decisions based on this data.