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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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Advances in Streamflow Forecasting

forecasting over the decades. Thereafter, chapters describe theoretical methodology of the different data-driven tools and techniques used for;

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

demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty.;

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

demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty.;

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Hydrological Data Driven Modelling

that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with;

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Machine Learning Methods in the Environmental Sciences

appendix listing websites for downloading computer code and data sources. A resources website contains datasets for exercises, and password;

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Machine Learning Methods in the Environmental Sciences

appendix listing websites for downloading computer code and data sources. A resources website contains datasets for exercises, and password;

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Real Estate Modelling & Forecasting

book introduces and explains a broad range of quantitative techniques that are relevant for the analysis of real estate data. It includes;

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Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

MLUE (Machine Learning for Uncertainty Estimation) approach is further explored and extended; in it the machine learning techniques (e.g;

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Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

MLUE (Machine Learning for Uncertainty Estimation) approach is further explored and extended; in it the machine learning techniques (e.g;

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Multicriteria Decision Aid And Artificial Intelligence

interested in the integration of MCDA and AI for the development of effective hybrid decision support methodologies and systems. Academics and post;

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Hydrological Drought Forecasting in Africa at Different Spatial and Temporal Scales

hydrological drought forecasting system makes use of a hydrological model that was found to be suitable for drought forecasting in Africa and could;

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Hydrological Drought Forecasting in Africa at Different Spatial and Temporal Scales

hydrological drought forecasting system makes use of a hydrological model that was found to be suitable for drought forecasting in Africa and could;

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Modelling to Program

discussion on novel approaches to programming based on modelling approaches such as model-driven development (MDE, MDA, MDD) and conceptual-model;

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A Practical Guide To Forecasting Financial Market Volatility

Financial market volatility forecasting is one of today's most important areas of expertise for professionals and academics in investment;

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Forecasting in Business and Economics

in economics and business. This is the only time series book to concentrate on the forecasting of economic data and to cover such a broad range;

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Advances in Conceptual Modeling

communicating conceptual models, techniques for transforming conceptual models into effective implementations, and the impact of conceptual modeling;

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Advances in Conceptual Modeling

modeling, methods and tools for developing and communicating conceptual models, techniques for transforming conceptual models into effective;

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Deterministic Methods in Systems Hydrology: Ihe Delft Lecture Note Series

chapters cover linear conceptual models of direct runoff, the fitting of conceptual models to data, simple models of subsurface flow and non-linear;

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Deterministic Methods in Systems Hydrology

chapters cover linear conceptual models of direct runoff, the fitting of conceptual models to data, simple models of subsurface flow and non-linear;

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A Primer on Machine Learning Applications in Civil Engineering

learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil;

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Economic Forecasting

coverage of methods and applications in this fast-growing area and is intended for use in postgraduate and upper-level undergraduate courses. Part;

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Cost Modelling

Cost models underlie all the techniques used in construction cost and price forecasting, yet until relatively recently industry has been;

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Business Forecasting

, business firms look for ways of improving their ability to gauge business and economic conditions around the world. At the same time, advances;

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Business Forecasting

, business firms look for ways of improving their ability to gauge business and economic conditions around the world. At the same time, advances;

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