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Practical Hydroinformatics Computational Intelligence and Technological Developments in Water Applications /

Hydroinformatics is an emerging subject that is expected to gather speed, momentum and critical mass throughout the forthcoming decades of the 21st century. This book provides a broad account of numerous advances in that field - a rapidly developing discipline covering the application of information...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Abrahart, Robert J. (Editor ), See, Linda M. (Editor ), Solomatine, Dimitri P. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
Edición:1st ed. 2008.
Colección:Water Science and Technology Library, 68
Temas:
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Hydroinformatics: Integrating Data and Models
  • Some Future Prospects in Hydroinformatics
  • Data-Driven Modelling: Concepts, Approaches and Experiences
  • Artificial Neural Network Models
  • Neural Network Hydroinformatics: Maintaining Scientific Rigour
  • Neural Network Solutions to Flood Estimation at Ungauged Sites
  • Rainfall-Runoff Modelling: Integrating Available Data and Modern Techniques
  • Dynamic Neural Networks for Nonstationary Hydrological Time Series Modeling
  • Visualisation of Hidden Neuron Behaviour in a Neural Network Rainfall-Runoff Model
  • Correction of Timing Errors of Artificial Neural Network Rainfall-Runoff Models
  • Data-Driven Streamflow Simulation: The Influence of Exogenous Variables and Temporal Resolution
  • Groundwater Table Estimation Using MODFLOW and Artificial Neural Networks
  • Neural Network Estimation of Suspended Sediment: Potential Pitfalls and Future Directions
  • Models Based on Fuzzy Logic
  • Fuzzy Logic-Based Approaches in Water Resource System Modelling
  • Fuzzy Rule-Based Flood Forecasting
  • Development of Rainfall-Runoff Models Using Mamdani-Type Fuzzy Inference Systems
  • Using an Adaptive Neuro-fuzzy Inference System in the Development of a Real-Time Expert System for Flood Forecasting
  • Building Decision Support Systems based on Fuzzy Inference
  • Global and Evolutionary Optimization
  • Global and Evolutionary Optimization for Water Management Problems
  • Conditional Estimation of Distributed Hydraulic Conductivity in Groundwater Inverse Modeling: Indicator-Generalized Parameterization and Natural Neighbors
  • Fitting Hydrological Models on Multiple Responses Using the Multiobjective Evolutionary Annealing-Simplex Approach
  • Evolutionary-based Meta-modelling: The Relevance of Using Approximate Models in Hydroinformatics
  • Hydrologic Model Calibration Using Evolutionary Optimisation
  • Randomised Search Optimisation Algorithms and Their Application in the Rehabilitation of Urban Drainage Systems
  • Neural Network Hydrological Modelling: An Evolutionary Approach
  • Emerging Technologies
  • Combining Machine Learning and Domain Knowledge in Modular Modelling
  • Precipitation Interception Modelling Using Machine Learning Methods - The Dragonja River Basin Case Study
  • Real-Time Flood Stage Forecasting Using Support Vector Regression
  • Learning Bayesian Networks from Deterministic Rainfall-Runoff Models and Monte Carlo Simulation
  • Toward Bridging the Gap Between Data-Driven and Mechanistic Models: Cluster-Based Neural Networks for Hydrologic Processes
  • Applications of Soft Computing to Environmental Hydroinformatics with Emphasis on Ecohydraulics Modelling
  • Data-Driven Models for Projecting Ocean Temperature Profile from Sea Surface Temperature
  • Model Integration
  • Uncertainty Propagation in Ensemble Rainfall Prediction Systems used for Operational Real-Time Flood Forecasting
  • OpenMI - Real Progress Towards Integrated Modelling
  • Hydroinformatics - The Challenge for Curriculum and Research, and the "Social Calibration" of Models
  • A New Systems Approach to Flood Management in the Yangtze River, China
  • Open Model Integration in Flood Forecasting.