Flood Forecasting Using Artificial Neural Networks

Flood Forecasting Using Artificial Neural Networks PDF Author: P. Varoonchotikul
Publisher: CRC Press
ISBN: 9781138475076
Category :
Languages : en
Pages :

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Book Description
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.

Flood Forecasting Using Artificial Neural Networks

Flood Forecasting Using Artificial Neural Networks PDF Author: P. Varoonchotikul
Publisher: CRC Press
ISBN: 9781138475076
Category :
Languages : en
Pages :

Get Book

Book Description
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.

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

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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.

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

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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.

Spatial Modeling in GIS and R for Earth and Environmental Sciences

Spatial Modeling in GIS and R for Earth and Environmental Sciences PDF Author: Hamid Reza Pourghasemi
Publisher: Elsevier
ISBN: 0128156953
Category : Mathematics
Languages : en
Pages : 798

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Book Description
Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions. The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling. Offers a clear, interdisciplinary guide to serve researchers in a variety of fields, including hazards, land surveying, remote sensing, cartography, geophysics, geology, natural resources, environment and geography Provides an overview, methods and case studies for each application Expresses concepts and methods at an appropriate level for both students and new users to learn by example

Proceedings of Second Doctoral Symposium on Computational Intelligence

Proceedings of Second Doctoral Symposium on Computational Intelligence PDF Author: Deepak Gupta
Publisher: Springer Nature
ISBN: 9811633460
Category : Technology & Engineering
Languages : en
Pages : 902

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Book Description
This book features high-quality research papers presented at Second Doctoral Symposium on Computational Intelligence (DoSCI-2021), organized by Institute of Engineering and Technology (IET), AKTU, Lucknow, India, on 6 March 2021. This book discusses the topics such as computational intelligence, artificial intelligence, deep learning, evolutionary algorithms, swarm intelligence, fuzzy sets and vague sets, rough set theoretic approaches, quantum-inspired computational intelligence, hybrid computational intelligence, machine learning, computer vision, soft computing, distributed computing, parallel and grid computing, cloud computing, high-performance computing, biomedical computing, decision support and decision making.

Geospatial Technologies for Land and Water Resources Management

Geospatial Technologies for Land and Water Resources Management PDF Author: Ashish Pandey
Publisher: Springer Nature
ISBN: 3030904792
Category : Computers
Languages : en
Pages : 617

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Book Description
This book focuses on the application of geospatial technologies to study the land use land cover (LULC) dynamics, agricultural water management, water resources assessment and modeling, and studies on natural disasters. LULC dynamics is one of the major research themes for studying global environmental change using remote sensing data. The section on LULC dynamics covers the multi-variate criteria for land use and land cover classification and change assessment in the mountainous regions. Further, LULC change detection of the Tons river basin and LULC dynamics at decadal frequency are studied to derive adaptation and mitigation strategies. Landscape-level forest disturbance modeling, together with conservation implications, is also included. The watershed management approach is necessary for comprehensive management of land and water resources of any region, where studies on multi-criteria analysis for rainwater harvesting planning and its impact on land use land cover transformations in rain-fed areas using geospatial technologies are presented in this book. The book will be useful for academics, water practitioners, scientists, water managers, environmentalists, and administrators, NGOs, researchers, and students who are actively involved in the application of geospatial technologies in LULC studies, agricultural water management and hydrological modelling and natural disasters for addressing the challenges being posed by climate change while addressing issues of food and water securities

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

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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.

Recent Trends in Civil Engineering

Recent Trends in Civil Engineering PDF Author: K. K. Pathak
Publisher: Springer Nature
ISBN: 9811551952
Category : Technology & Engineering
Languages : en
Pages : 1081

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Book Description
This book presents the selected peer-reviewed proceedings of the International Conference on Recent Trends and Innovations in Civil Engineering (ICRTICE 2019). The volume focuses on latest research and advances in the field of civil engineering and materials science such as design and development of new environmental materials, performance testing and verification of smart materials, performance analysis and simulation of steel structures, design and performance optimization of concrete structures, and building materials analysis. The book also covers studies in geotechnical engineering, hydraulic engineering, road and bridge engineering, building services design, engineering management, water resource engineering and renewable energy. The contents of this book will be useful for students, researchers and professionals working in civil engineering.

Artificial Neural Network Modelling

Artificial Neural Network Modelling PDF Author: Subana Shanmuganathan
Publisher: Springer
ISBN: 3319284959
Category : Technology & Engineering
Languages : en
Pages : 472

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Book Description
This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.

Research Anthology on Artificial Neural Network Applications

Research Anthology on Artificial Neural Network Applications PDF Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1668424096
Category : Computers
Languages : en
Pages : 1575

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Book Description
Artificial neural networks (ANNs) present many benefits in analyzing complex data in a proficient manner. As an effective and efficient problem-solving method, ANNs are incredibly useful in many different fields. From education to medicine and banking to engineering, artificial neural networks are a growing phenomenon as more realize the plethora of uses and benefits they provide. Due to their complexity, it is vital for researchers to understand ANN capabilities in various fields. The Research Anthology on Artificial Neural Network Applications covers critical topics related to artificial neural networks and their multitude of applications in a number of diverse areas including medicine, finance, operations research, business, social media, security, and more. Covering everything from the applications and uses of artificial neural networks to deep learning and non-linear problems, this book is ideal for computer scientists, IT specialists, data scientists, technologists, business owners, engineers, government agencies, researchers, academicians, and students, as well as anyone who is interested in learning more about how artificial neural networks can be used across a wide range of fields.