Hybrid models for Hydrological Forecasting: integration of data-driven and conceptual modelling techniques : UNESCO-IHE PhD Thesis

Gerald Augusto Corzo Perez

ISBN 10: 0415565979 / ISBN 13: 9780415565974

Edité par Taylor & Francis Ltd Jan 2010, 2010

A propos de cet article

Neuware - 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.

N° de réf. du vendeur 9780415565974

Détails bibliographiques

• Titre: Hybrid models for Hydrological Forecasting: integration of data-driven and conceptual modelling techniques : UNESCO-IHE PhD Thesis
• Éditeur: Taylor & Francis Ltd Jan 2010
• Date de parution: 2010
• Langue: anglais
• ISBN à 10 chiffres: 0415565979
• ISBN à 13 chiffres: 9780415565974
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 244 L x 171 H x 14 P (en millimètres) 244 millimètres de largeur par 171 millimètres de hauteur par 14 millimètres de profondeur
• Poids: 404 grammes

A propos de ce titre

Synopsis

This book presents the investigation of possibilities and different architectures of integrating hydrological knowledge and conceptual models with data-driven models for the purpose of hydrological flow forecasting. Models resulting from such integration are referred to as hybrid models. The book addresses the following specific topics:

• A classification of different hybrid modelling approaches in the context of flow forecasting
• The methodological development and application of modular models based on clustering and baseflow empirical formulations
• The integration of hydrological conceptual models with neural network error corrector models and the use of committee models for daily streamflow forecasting
• The application of modular modelling and fuzzy committee models to the problem of downscaling weather information for hydrological forecasting

The results of this research show the increased forecasting accuracy when modular models, which integrate conceptual and data-driven models, are considered. Committee machine modelling show to be able to manage increased lead time with an acceptable accuracy.

À propos de l?auteur

Gerald Corzo received his degree of civil engineering from the Escuela Colombian de Ingenieria, Faculty of Engineering (Bogotá, Colombia). He joined the Universidad Francisco de Paula Santander (UFPS), as a lecturer in Mathematics and Statistics (2000-2003). After graduating with the highest degree in his course he moved to Bogotá and served as lecturer in different universities. In October 2003 he moved to Delft in the Netherlands, there he joined the Hydroinformatics Master program at the UNESCO-IHE Institute for Water Education. He was awarded Master of Science in May 2005. His work explored the use of rule-based modelling and Committee of data-driven models in hydrological forecasting. In his thesis he showed a modular scheme approaching the incorporation of hydrological knowledge in a committee model. He continued with his work in the Phd, where he extended the analysis of artificial intelligence methods applied to hydrological phenomena.