Emotional ANN (EANN): A New Generation of Neural Networks for Hydrological Modeling in IoT

2019 ◽  
pp. 45-61 ◽  
Author(s):  
Vahid Nourani ◽  
Amir Molajou ◽  
Hessam Najafi ◽  
Ali Danandeh Mehr
Keyword(s):  
Neural Networks ◽  
Hydrological Modeling ◽  
New Generation

scholarly journals Deep neural networks in hydrology: the new generation of universal and efficient models

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Georgy V. Ayzel ◽  
◽  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Hydrological Modeling ◽  
Recent Change ◽  
Model Parameters ◽  
Learning Technology ◽  
Water Balance Components ◽  
Wide Range ◽  
New Generation

For around a decade, deep learning – the sub-field of machine learning that refers to artificial neural networks comprised of many computational layers – modifies the landscape of statistical model development in many research areas, such as image classification, machine translation, and speech recognition. Geoscientific disciplines in general and the field of hydrology in particular, also do not stand aside from this movement. Recently, the proliferation of modern deep learning-based techniques and methods has been actively gaining popularity for solving a wide range of hydrological problems: modeling and forecasting of river runoff, hydrological model parameters regionalization, assessment of available water resources, identification of the main drivers of the recent change in water balance components. This growing popularity of deep neural networks is primarily due to their high universality and efficiency. The presented qualities, together with the rapidly growing amount of accumulated environmental information, as well as increasing availability of computing facilities and resources, allow us to speak about deep neural networks as a new generation of mathematical models designed to, if not to replace existing solutions, but significantly enrich the field of geophysical processes modeling. This paper provides a brief overview of the current state of the field of development and application of deep neural networks in hydrology. Also in the following study, the qualitative long-term forecast regarding the development of deep learning technology for managing the corresponding hydrological modeling challenges is provided based on the use of “Gartner Hype Curve”, which in the general details describes a life cycle of modern technologies.

Artificial Neural Networks

2010 ◽  
pp. 122-128 ◽  
Author(s):  
Xiaojun Yang
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Urban Growth ◽  
Hydrological Modeling ◽  
Basic Structure ◽  
Nonlinear Phenomena ◽  
Model Complex ◽  
Artificial Neural ◽  
Multilayer Perceptron Networks

Artificial neural networks are increasingly being used to model complex, nonlinear phenomena. The purpose of this chapter is to review the fundamentals of artificial neural networks and their major applications in geoinformatics. It begins with a discussion on the basic structure of artificial neural networks with the focus on the multilayer perceptron networks given their robustness and popularity. This is followed by a review on the major applications of artificial neural networks in geoinformatics, including pattern recognition and image classification, hydrological modeling, and urban growth prediction. Finally, several areas are identified for further research in order to improve the success of artificial neural networks for problem solving in geoinformatics.

Lighting up neural networks using a new generation of genetically encoded calcium sensors

2009 ◽  
Vol 6 (12) ◽  
pp. 871-872 ◽  
Author(s):  
Christian D Wilms ◽  
Michael Häusser
Keyword(s):  
Neural Networks ◽  
Calcium Sensors ◽  
New Generation

Cerebral Organization of Music Processing

2018 ◽  
pp. 87-121
Author(s):  
Thenille Braun Janzen ◽  
Michael H. Thaut
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Current Knowledge ◽  
Brain Networks ◽  
Music Listening ◽  
Functional Basis ◽  
Functional Interactions ◽  
The Past ◽  
Neuroscience Research ◽  
New Generation

This chapter presents a broad panorama of the current knowledge concerning the anatomical and functional basis of music processing in the healthy brain. Neuroimaging studies developed over the past 20 years provide evidence that music processing takes place in widely distributed neural networks. Here, attention is focused on core brain networks implicated in music processing, emphasizing the anatomical and functional interactions between cortical and subcortical areas within auditory-frontal networks, auditory-motor networks, and auditory-limbic networks. Finally, the authors review recent studies investigating how brain networks organize themselves in a naturalistic music listening context. Collectively, this robust body of literature demonstrates that music processing requires timely coordination of large-scale cognitive, motor, and limbic brain networks, setting the stage for a new generation of music neuroscience research on the dynamic organization of brain networks underlying music processing.

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