Artículos relacionados a Hydrological Predictions: Using Data-Driven Models...

Hydrological Predictions: Using Data-Driven Models Coupled with Data Preprocessing Techniques - Tapa blanda

Chau, Kwok-wing; Wu, Cong-lin

 
9783843364461: Hydrological Predictions: Using Data-Driven Models Coupled with Data Preprocessing Techniques
Tapa blanda
ISBN 10: 384336446X ISBN 13: 9783843364461
Editorial: LAP LAMBERT Academic Publishing, 2011

Sinopsis

This book makes an endeavor to improve the accuracy of hydrological forecasting in three aspects, model inputs, selection of models, and data-preprocessing techniques. Seven input techniques, namely, linear correlation analysis (LCA), false nearest neighbors, correlation integral, stepwise linear regression, average mutual information, partial mutual information, artificial neural network (ANN) based on multi-objective genetic algorithm, are first examined to select optimal model inputs in each prediction scenario. Representative models, such as K-nearest-neighbors (K-NN) model, dynamic system based model (DSBM), ANN, modular ANN (MANN), and hybrid artificial neural network-support vector regression (ANN-SVR), are then proposed to conduct rainfall and streamflow forecasts. Four data-preprocessing methods including moving average (MA), principal component analysis (PCA), singular spectrum analysis (SSA), and wavelet analysis (WA), are further investigated by integration with the abovementioned forecasting models.

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

This book makes an endeavor to improve the accuracy of hydrological forecasting in three aspects, model inputs, selection of models, and data-preprocessing techniques. Seven input techniques, namely, linear correlation analysis (LCA), false nearest neighbors, correlation integral, stepwise linear regression, average mutual information, partial mutual information, artificial neural network (ANN) based on multi-objective genetic algorithm, are first examined to select optimal model inputs in each prediction scenario. Representative models, such as K-nearest-neighbors (K-NN) model, dynamic system based model (DSBM), ANN, modular ANN (MANN), and hybrid artificial neural network-support vector regression (ANN-SVR), are then proposed to conduct rainfall and streamflow forecasts. Four data-preprocessing methods including moving average (MA), principal component analysis (PCA), singular spectrum analysis (SSA), and wavelet analysis (WA), are further investigated by integration with the abovementioned forecasting models.

Biografía del autor

Kwok-wing Chau, PhD: Studied Civil Engineering at University of Hong Kong and University of Queensland. Professor at The Hong Kong Polytechnic University, Hong Kong. Cong-lin Wu, PhD: Studied Hydrologic Engineering at The Hong Kong Polytechnic University. Currently working at Changjiang Water Resources Commission, People's Republic of China.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
LAP LAMBERT Academic Publishing
Año de publicación
2011
Idioma
Inglés
ISBN 10 
384336446X
ISBN 13 
9783843364461
Encuadernación
Tapa blanda
Número de páginas
248
Contacto del fabricante
no disponible
Persona responsable
Buchpark GmbH
gpsr@buchpark.de

Krügerweg 1
Trebbin
Brandenburg
14959
Alemania