Author
Makarand Deo
Other affiliations: Carleton University, University of Toledo, University of Michigan ...read more
Bio: Makarand Deo is an academic researcher from Norfolk State University. The author has contributed to research in topics: Artificial neural network & Wave height. The author has an hindex of 35, co-authored 150 publications receiving 4540 citations. Previous affiliations of Makarand Deo include Carleton University & University of Toledo.
Papers published on a yearly basis
Papers
More filters
TL;DR: An overview of the bidomain equations and the methods by which they have been solved is given, of particular note are recent developments in multigrid methods, which have proven to be the most efficient.
Abstract: The bidomain equations are widely used for the simulation of electrical activity in cardiac tissue. They are especially important for accurately modeling extracellular stimulation, as evidenced by their prediction of virtual electrode polarization before experimental verification. However, solution of the equations is computationally expensive due to the fine spatial and temporal discretization needed. This limits the size and duration of the problem which can be modeled. Regardless of the specific form into which they are cast, the computational bottleneck becomes the repeated solution of a large, linear system. The purpose of this review is to give an overview of the equations and the methods by which they have been solved. Of particular note are recent developments in multigrid methods, which have proven to be the most efficient.
298 citations
TL;DR: The present work employs the technique of neural networks in order to forecast daily, weekly as well as monthly wind speeds at two coastal locations in India and is found to be more accurate than traditional statistical time-series analysis.
296 citations
TL;DR: This paper presents a complementary and simple method to make a point forecast of waves in real time sense based on the current observation of waves at a site that incorporates the technique of neural networks.
287 citations
TL;DR: Neural networks are highlighted in real-time forecasting of water levels at a given site continuously throughout the year based on the same levels at some upstream gauging station and/or using the stage time history recorded at the same site.
Abstract: Methods to continuously forecast water levels at a site along a river are generally model based. Physical processes influencing occurrence of a river stage are, however, highly complex and uncertain, which makes it difficult to capture them in some form of deterministic or statistical model. Neural networks provide model-free solutions, and hence can be expected to be appropriate in these conditions. Built-in dynamism in forecasting, data-error tolerance, and lack of requirements of any exogenous input are additional attractive features of neural networks. This paper highlights their use in real-time forecasting of water levels at a given site continuously throughout the year based on the same levels at some upstream gauging station and/or using the stage time history recorded at the same site. The network is trained by using three algorithms, namely, error back propagation, cascade correlation, and conjugate gradient. The training results are compared with each other. The network is verified with untrained data.
269 citations
TL;DR: A simple 3-layered feed forward type of network is developed and shows that an appropriately trained network could provide satisfactory results in open wider areas, in deep water and also when the sampling and prediction interval is large, such as a week.
249 citations
Cited by
More filters
TL;DR: The steps that should be followed in the development of artificial neural network models are outlined, including the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation.
Abstract: Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water resources variables. In this paper, the steps that should be followed in the development of such models are outlined. These include the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation. The options available to modellers at each of these steps are discussed and the issues that should be considered are highlighted. A review of 43 papers dealing with the use of neural network models for the prediction and forecasting of water resources variables is undertaken in terms of the modelling process adopted. In all but two of the papers reviewed, feedforward networks are used. The vast majority of these networks are trained using the backpropagation algorithm. Issues in relation to the optimal division of the available data, data pre-processing and the choice of appropriate model inputs are seldom considered. In addition, the process of choosing appropriate stopping criteria and optimising network geometry and internal network parameters is generally described poorly or carried out inadequately. All of the above factors can result in non-optimal model performance and an inability to draw meaningful comparisons between different models. Future research efforts should be directed towards the development of guidelines which assist with the development of ANN models and the choice of when ANNs should be used in preference to alternative approaches, the assessment of methods for extracting the knowledge that is contained in the connection weights of trained ANNs and the incorporation of uncertainty into ANN models.
2,181 citations
Johns Hopkins University1, Leipzig University2, Korea University3, Yale University4, West Virginia University5, University of Barcelona6, St George's, University of London7, Indiana University8, National Yang-Ming University9, Cleveland Clinic10, Aarhus University11, University at Buffalo12, Imperial College London13, Primary Children's Hospital14, Erasmus University Rotterdam15, Yeshiva University16, Ghent University17, Baylor University18, Virginia Commonwealth University19, Harvard University20, Federal University of São Paulo21, University of California, San Francisco22, Beaumont Hospital23, Boston University24, University of Oklahoma25, Carlos III Health Institute26, University of Michigan27, University of Melbourne28, Saint Louis University29, Université de Montréal30, University of Pennsylvania31, McGill University32, Mayo Clinic33, Lahey Hospital & Medical Center34, Royal Adelaide Hospital35, University of Milan36, University of Toronto37, Loyola University Chicago38, Jikei University School of Medicine39
TL;DR: This 2017 Consensus Statement is to provide a state-of-the-art review of the field of catheter and surgical ablation of AF and to report the findings of a writing group, convened by these five international societies.
1,626 citations
TL;DR: In this article, the authors investigate the role of artificial neural networks (ANNs) in hydrology and show that ANNs are gaining popularity, as is evidenced by the increasing number of papers on this topic.
Abstract: In this two-part series, the writers investigate the role of artificial neural networks (ANNs) in hydrology. ANNs are gaining popularity, as is evidenced by the increasing number of papers on this ...
1,334 citations
TL;DR: The role of ANNs in various branches of hydrology has been examined here and it is suggested that ANNs should be considered as a “bridge network” to other types of neural networks.
Abstract: This paper forms the second part of the series on application of artificial neural networks (ANNs) in hydrology. The role of ANNs in various branches of hydrology has been examined here. It is foun...
1,106 citations
TL;DR: A template is proposed in order to assist the construction of future ANN rainfall-runoff models and it is suggested that research might focus on the extraction of hydrological ‘rules’ from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.
Abstract: This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting. This is an emerging field of research, character- ized by a wide variety of techniques, a diversity of geographical contexts, a general absence of intermodel comparisons, and inconsistent reporting of model skill. This article begins by outlining the basic principles of ANN modelling, common network architectures and training algorithms. The discussion then addresses related themes of the division and preprocessing of data for model calibration/validation; data standardization techniques; and methods of evaluating ANN model performance. A literature survey underlines the need for clear guidance in current modelling practice, as well as the comparison of ANN methods with more conven- tional statistical models. Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models. Finally, it is suggested that research might focus on the extraction of hydrological 'rules' from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.
813 citations