Hydrological Data Driven Modelling

This book PDF is perfect for those who love Science genre, written by Renji Remesan and published by Springer which was released on 03 November 2014 with total hardcover pages 250. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Hydrological Data Driven Modelling books below.

Hydrological Data Driven Modelling
Author : Renji Remesan
File Size : 54,7 Mb
Publisher : Springer
Language : English
Release Date : 03 November 2014
ISBN : 9783319092355
Pages : 250 pages
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Hydrological Data Driven Modelling by Renji Remesan Book PDF Summary

This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.

Hydrological Data Driven Modelling

This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing

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Advances in Data based Approaches for Hydrologic Modeling and Forecasting

This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods.

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Information Theory and Artificial Intelligence to Manage Uncertainty in Hydrodynamic and Hydrological Models

The complementary nature of physically-based and data-driven models in their demand for physical insight and historical data, leads to the notion that the predictions of a physically-based model can be improved and the associated uncertainty can be systematically reduced through the conjunctive use of a data-driven model of the residuals.

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Distributed Hydrological Modelling

It is the task of the engineer, as of any other professional person, to do everything that is reasonably possible to analyse the difficulties with which his or her client is confronted, and on this basis to design solutions and implement these in practice. The distributed hydrological model is, correspondingly,

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