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Hybrid models for Hydrological Forecasting: integration of data-driven and conceptual modelling techniques

models with data-driven models for the purpose of hydrological flow forecasting. Models resulting from such integration are referred to as hybrid;

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Hybrid models for Hydrological Forecasting: integration of data-driven and conceptual modelling techniques

models with data-driven models for the purpose of hydrological flow forecasting. Models resulting from such integration are referred to as hybrid;

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Integrating Multiple Sources of Information for Improving Hydrological Modelling

The availability of Earth observation and numerical weather prediction data for hydrological modelling and water management has increased;

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Spatiotemporal Analysis of Extreme Hydrological Events

Spatio-temporal Analysis of Extreme Hydrological Events offers an extensive view of the experiences and applications of the latest;

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Scale Issues in Hydrological Modelling

time scales. Theories of non-linear hydrological processes must be extrapolated to large-scale, three-dimensional natural systems such as;

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Statistical Analysis and Stochastic Modelling of Hydrological Extremes

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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;

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Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately;

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Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately;

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Environmental and Hydrological Systems Modelling

have a choice when confronted with constraints such as lack of sufficient data, resources, expertise and time. Environmental and;

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Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques

This book describes the use of machine learning techniques to build predictive models of uncertainty with application to hydrological;

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Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques

This book describes the use of machine learning techniques to build predictive models of uncertainty with application to hydrological;

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Satellite Remote Sensing in Hydrological Data Assimilation

This book presents the fundamentals of data assimilation and reviews the application of satellite remote sensing in hydrological data;

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Machine Learning Methods in the Environmental Sciences

sciences. Due to their powerful nonlinear modelling capability, machine learning methods today are used in satellite data processing, general;

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Machine Learning Methods in the Environmental Sciences

sciences. Due to their powerful nonlinear modelling capability, machine learning methods today are used in satellite data processing, general;

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Catchment Hydrological Modelling

Catchment Hydrological Modelling: The Science and Art covers various methods (and equations) for modeling all components of a CHM;

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Mobility Data Driven Urban Traffic Monitoring

This book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based;

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Hydrological Drought Forecasting in Africa at Different Spatial and Temporal Scales

modelling framework for hydrological drought forecasting in sub-Saharan Africa as a step towards an effective early warning system. The proposed;

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Hydrological Drought Forecasting in Africa at Different Spatial and Temporal Scales

modelling framework for hydrological drought forecasting in sub-Saharan Africa as a step towards an effective early warning system. The proposed;

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Distributed Hydrological Modelling

terrain data be used in hydrological modelling to improve the realism of the predictions? There are not many choices available: Topmodel is one;

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Hydroinformatics

hydrological data available today. Linking hydrological science with computer engineering, networking, and database science, this book lays a;

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Hydroinformatics

hydrological data available today. Linking hydrological science with computer engineering, networking, and database science, this book lays a;

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Advances in Applications of Data Driven Computing

This book aims to foster machine and deep learning approaches to data-driven applications, in which data governs the behaviour;

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Solute Modelling in Catchment Systems

of the book is to improve links between biological, chemical and hydrological models. Models are examined which can be used to assess the;

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Cyber-Physical Systems

This book presents new findings on cyber-physical systems design and modelling approaches based on AI and data-driven techniques;

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Cyber-Physical Systems

This book presents new findings on cyber-physical systems design and modelling approaches based on AI and data-driven techniques;

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Data Analysis Using Hierarchical Generalized Linear Models with R

within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to;

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