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Dynamic Data Assimilation - Beating the Uncertainties
| Content Provider | Faculty Contribution |
|---|---|
| Author | Harkut, Dinesh G. |
| Abstract | Our life is highly influenced and affected by the uncertainty in predicting outcome of various phenomena and human activities. Data Assimilation is basically process of fusing data with the model for the singular purpose of estimating the unknown variables. One can obtain an instantiation of the model once these estimates are available, which in turn then run forward in time to generate the requisite forecast products for public consumption. Data Assimilation can be used for multiple purposes: to estimate the optimal state of a model, to estimate the initial state of a system in order to use it to predict the future state of the system. Predicting the evolution of the atmosphere is a complicated problem that requires the most accurate initial conditions to obtain an accurate estimate of the atmospheric state variables at a given time and point. Though traditional data assimilation methods introduces Kalman filters and variational approaches, application of Artificial Intelligence, Neural Network, Machine Learning, Cognitive computing can be exploited further to forecast by accommodating the dynamics of model to obtain the most critical initial condition precisely. Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. |
| File Format | |
| Language | English |
| Publisher | IntechOpen Limited, London, UK |
| Publisher Department | Department of Computer Science & Engineering |
| Publisher Institution | Prof Ram Meghe College of Engineering & Management |
| Access Restriction | Open |
| Subject Keyword | Machine Learning (ML) Feed Forward Back Propagation Markovian processes Spatio-temporal domain Data assimilation Kalman filters Artificial Neural Networks (ANN) |
| Content Type | Text |
| Educational Use | Reading |
| Resource Type | Book |
| Education Level | Under Graduate Post Graduate |
| Subject | Special computer methods |
