| Description |
viii, 215 pages : illustrations (some color), maps (some color) ; 25 cm |
| Contents |
Contents note continued: 3.6.Modularization using spatial-based partitioning -- 3.7.Optimal combination of modularization schemes -- 3.8.Conclusions -- 4.Building data-driven hydrological models: data issues -- 4.1.Introduction -- 4.2.Case study (Ourthe river basin - Belgium) -- 4.3.Procedure of data-driven modelling -- 4.4.Preparing data and building a model -- 4.5.The problem of input variables selection -- 4.5.1.Inputs selection based on correlation analysis -- 4.5.2.Selection based on Average Mutual Information (AMI) -- 4.6.Influence of data partitioning -- 4.7.Influence of ANN weight initialization -- 4.7.1.Models not using past discharges as inputs (RR) -- 4.7.2.Models using past discharges as inputs (RRQ) -- 4.8.Various measures of model error -- 4.9.Comparing the various types of models -- 4.10.Discussion and conclusions -- 5.Time and process based modularization -- 5.1.Introduction -- 5.2.Catchment descriptions -- 5.3.Input variable selection -- |
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Contents note continued: 5.4.Comparison to benchmark models -- 5.5.Modelling process -- 5.6.Results and discussion -- 5.7.Conclusions -- 6.Spatial-based hybrid modelling -- 6.1.Introduction -- 6.2.HBV-M model for Meuse river basin -- 6.2.1.Characterisation of the Meuse River basin -- 6.2.2.Data validation -- 6.3.Methodology -- 6.3.1.HBV-M model setup -- 6.3.2.Scheme 1: Sub-basin model replacement -- 6.3.3.Scheme 2: Integration of sub-basin models -- 6.4.Application of Scheme 1 -- 6.4.1.Inputs selection and data preparation for DDMs -- 6.4.2.Data-driven sub-basin models -- 6.4.3.Analysis of HBV-S simulation errors -- 6.4.4.Replacements of sub-basin models by ANNs -- 6.5.Application of Scheme 2 -- 6.6.Discussion -- 6.6.1.Scheme 1 -- 6.6.2.Scheme 2 -- 6.7.Conclusions -- 7.Hybrid parallel and sequential models -- 7.1.Introduction -- 7.2.Metodology and models setup -- 7.2.1.Meuse river basin data and HBV model -- 7.2.2.ANN model setup -- 7.3.Data assimilation (error correction) -- |
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Contents note continued: 7.4.Committee and ensemble models -- 7.5.Forecasting scenario -- 7.6.Results and discussion -- 7.6.1.Single forecast results -- 7.6.2.Results on multi step forecast -- 7.7.Conclusions -- 8.Downscalling with modular models -- 8.1.Introduction -- 8.2.Fuzzy committee -- 8.3.Case study: Beles River Basin, Ethiopia -- 8.4.Beles River Basin -- 8.5.Methodology -- 8.5.1.ANN model setup -- 8.5.2.Committee and modular models -- 8.5.3.Fuzzy committee machine -- 8.6.Results -- 8.7.Conclusions -- 9.Conclusions and Recommendations -- 9.1.Hybrid modelling -- 9.2.Modular modelling -- 9.3.Downscaling with modular models -- 9.4.Parallel and serial modelling architectures -- 9.5.Data-driven modelling -- 9.6.Conclusion in brief -- Bibliography -- A.State-Space to input-output transformation -- A.1.State-space and input-output models -- B.Data-driven Models -- B.1.Artificial Neural Networks (Multi-layer perceptron) -- B.2.Model Trees (M5P) -- B.3.Support Vector Machines -- |
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Contents note continued: C.Hourly forecast models in the Meuse -- C.1.Methodology -- C.2.Neural network model (ANN) -- C.3.Results |
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Machine generated contents note: 1.Introduction -- 1.1.Background -- 1.2.Flood management and forecasting -- 1.2.1.Flood management measures -- 1.2.2.Operational flow forecasting -- 1.3.Hydrological models -- 1.3.1.Classification -- 1.3.2.HBV process-based model -- 1.4.Data-driven models -- 1.5.Objectives of the research -- 1.6.Terminology -- 1.7.Outline -- 2.Framework for hybrid modeling -- 2.1.Introduction -- 2.2.General considerations and assumptions -- 2.3.Hybrid modelling framework -- 2.3.1.Classification of hybrid models -- 2.3.2.Relationships between model classes -- 2.4.Committee machines and modular models -- 2.5.Measuring model performance -- 2.6.Discussion and conclusions -- 3.Optimal modularization of data-driven models -- 3.1.Introduction -- 3.2.Methodology of modular modelling -- 3.3.Modularization using clustering (MM1) -- 3.4.Modularization using sub-process identification (MM2) -- 3.5.Modularization using time-based partitioning (MM3) -- |
| Notes |
Formerly CIP. Uk |
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Thesis (Doctoral)--Delft University of Technology ; UNESCO-IHE Institute for Water Education, 2009 |
| Bibliography |
Includes bibliographical references |
| Subject |
Hydrologic models.
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Hydrological forecasting.
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| Author |
Technische Universiteit Delft.
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Unesco-IHE Institute for Water Education.
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| ISBN |
0415565979 (paperback) |
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9780415565974 (paperback) |
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