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CRC Press Uncertainty Analysis in Rainfall-Runoff Modelling

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Description

Explore advanced hydrological modeling with Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques. This UNESCO-IHE PhD Thesis, published by CRC Press, details how machine learning techniques can build predictive models of uncertainty for hydrological applications. The text focuses on two primary modeling approaches. The first method addresses parameter uncertainty by using efficient machine learning techniques to emulate Monte Carlo simulation results. The second method builds an ensemble of specialized machine learning models based on the past performance of hydrological models. This book provides a technical foundation for researchers and professionals looking to apply machine learning to improve the accuracy and reliability of rainfall-runoff predictions.

Key Features

Published by CRC Press, this UNESCO-IHE PhD Thesis provides a deep look into machine learning applications for hydrological models.

Learn how to use efficient machine learning techniques to emulate Monte Carlo simulation results for parameter uncertainty analysis.

Discover methods for building an ensemble of specialized machine learning models based on historical hydrological model performance.

The content focuses on developing and testing predictive models to manage uncertainty in rainfall-runoff modeling.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
20 July 2017
Listed Since
26 July 2017

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