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

Chau, Kwok-wing; Wu, Cong-lin

Softcover

ISBN 10: 384336446X ISBN 13: 9783843364461

Publisher: LAP LAMBERT Academic Publishing, 2011

Synopsis

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.

About this title

About the Author

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.

Edition details

• Publisher: LAP LAMBERT Academic Publishing
• Publication date: 2011
• Language: English
• ISBN 10: 384336446X
• ISBN 13: 9783843364461
• Binding: Paperback
• Number of pages: 248