Hybrid models for Hydrological Forecasting

Discusses possibilities and different architectures for integrating hydrological knowledge and conceptual models with data-driven models for use in hydrological flow forecasting. This book explores a classification of different hybrid modeling approaches, the methodological development and application of modular models.

Summary 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) 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 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) 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 Downscaling 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 Arti-cial Neural Networks (Multi-layer perceptron) B.2 Model Trees (M5P) B.3 Support Vector Machines C Hourly forecast models in the Meuse C.1 Methodology C.2 Neural network model (ANN) C.3 Results List of Figures List of Tables List of acronyms Samenvatting Acknowledgements About the author