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.

Liquid retaining structures are more vulnerable to corrosion problems and thus have stringent requirements against serviceability limit state of crack. The design procedures of these structures require significant empirical inputs from specialists. With the recent advent of artificial intelligence technology, a coupled knowledge-based system can handle both the symbolic knowledge processing based on engineering heuristics in the preliminary synthesis stage and the extensive numerical crunching involved in the detailed structural analysis stage. This book presents a prototype coupled knowledge-based system for the design of liquid retaining structures...