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Author: Jean-Yves Djamen-Kepaou Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
"Global climate models represent major climate system components of the planet in order to generate long term, sparse, accurate realizations of future climatic events across the entire globe. Downscaling is the method by which these low resolution realizations are converted into high resolution simulations of climate events which can then be used by stakeholders and policy makers. Regional climate models dynamically downscale simulated climate by conditioning global climate models on location-specific physical processes. Although these models are robust and reliable, they are computationally expensive when compared to statistical approaches for modeling a general relationship between global climate behaviour and local climate behavior. Therefore, there is need for downscaling methods that leverage the computational efficiency of statistical models while maintaining the performance of regional climate models.In this thesis, we build upon previously proposed deep learning methods for dynamical downscaling through estimation of a regional climate model. Our proposed model is a generative adversarial network that leverages the effects of temporal dependencies within spatio-temporal climate events"--
Author: Jean-Yves Djamen-Kepaou Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
"Global climate models represent major climate system components of the planet in order to generate long term, sparse, accurate realizations of future climatic events across the entire globe. Downscaling is the method by which these low resolution realizations are converted into high resolution simulations of climate events which can then be used by stakeholders and policy makers. Regional climate models dynamically downscale simulated climate by conditioning global climate models on location-specific physical processes. Although these models are robust and reliable, they are computationally expensive when compared to statistical approaches for modeling a general relationship between global climate behaviour and local climate behavior. Therefore, there is need for downscaling methods that leverage the computational efficiency of statistical models while maintaining the performance of regional climate models.In this thesis, we build upon previously proposed deep learning methods for dynamical downscaling through estimation of a regional climate model. Our proposed model is a generative adversarial network that leverages the effects of temporal dependencies within spatio-temporal climate events"--
Author: Taesam Lee Publisher: Springer Nature ISBN: 3030647773 Category : Science Languages : en Pages : 215
Book Description
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.
Author: Siddhartha Bhattacharyya Publisher: Walter de Gruyter GmbH & Co KG ISBN: 3110670909 Category : Computers Languages : en Pages : 161
Book Description
This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.
Author: Sharad K. Jain Publisher: Elsevier ISBN: 0323984126 Category : Science Languages : en Pages : 956
Book Description
Water Resources Systems Planning and Management, Second Edition, Volume 51 presents new and updated material, including case studies, examples and important updates on topics such as climate change and integrated water resources management. Authored by two renowned experts in the field of water resources, this text provides an overview of the current status of water resources utilization, the likely scenario of future demands, simulation and techniques of economic analysis, concepts of planning, the planning process, integrated planning, public involvement, reservoir sizing, and finally, systems operation and management. This book presents a comprehensive overview of the field that is relevant for students, professors, scholars, researchers, and consultants in the fields of Water Resources, Civil Engineering, Environmental Engineering and Hydrology. Provides an overview of the current status of water resources utilization, the likely scenario of future demands, and the advantages and disadvantages of systems techniques Includes numerous examples and real-world case studies Discusses the concepts of planning, the planning process, integrated planning, public involvement, and reservoir sizing
Author: Shai Avidan Publisher: Springer Nature ISBN: 3031197976 Category : Computers Languages : en Pages : 812
Book Description
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Author: Pierre Comon Publisher: Academic Press ISBN: 0080884946 Category : Technology & Engineering Languages : en Pages : 856
Book Description
Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, R&D engineers and graduates wishing to learn the core principles, methods, algorithms, and applications of Blind Source Separation. Covers the principles and major techniques and methods in one book Edited by the pioneers in the field with contributions from 34 of the world’s experts Describes the main existing numerical algorithms and gives practical advice on their design Covers the latest cutting edge topics: second order methods; algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communications Shows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications
Author: M. Arif Wani Publisher: Springer ISBN: 9789811567582 Category : Technology & Engineering Languages : en Pages : 300
Book Description
This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.
Author: Jean-Yves Djamen-Kepaou
Author: Jean-Yves Djamen-Kepaou
Author: Gaurav Tripathi
Author: Taesam Lee
Author: Xander Wang
Author: Siddhartha Bhattacharyya
Author: Dánnell Quesada Chacón
Author: Sharad K. Jain
Author: Shai Avidan
Author: Pierre Comon
Author: M. Arif Wani