Preface; Author; 1 Introduction; 1.1 Some definitions; 1.2 General systems theory (GST); 1.3 Ecological systems (Ecosystems); 1.4 Equi-finality; 1.5 Scope and layout; References; 2 Historical development of hydrological modelling; 2.1 Basic concepts and governing equation of linear systems; 2.2 Linear systems in hydrological modelling; 2.3 Random processes and linear systems; 2.4 Non-linear systems; 2.5 Multilinear or parallel systems; 2.6 Flood routing; 2.7 Reservoir routing; 2.8 Rainfall-runoff modelling; 2.9 Guiding principles and criteria for choosing a model
5.2 Water quality in a completely mixed water body5.3 Water quality in rivers and streams; 5.4 Concluding remarks; References; 6 Longitudinal dispersion; 6.1 Introduction; 6.2 Governing equations; 6.3 Dispersion coefficient; 6.4 Numerical solution; 6.5 Dispersion through porous media; 6.6 General-purpose water quality models; 6.7 Concluding remarks; References; 7 Time series analysis and forecasting; 7.1 Introduction; 7.2 Basic properties of a time series; 7.3 Statistical parameters of a time series; 7.4 Tests for stationarity; 7.5 Tests for homogeneity; 7.6 Components of a time series
7.7 Trend analysis7.8 Periodicity; 7.9 Stochastic component; 7.10 Residual series; 7.11 Forecasting; 7.12 Synthetic data generation; 7.13 ARMAX modelling; 7.14 Kalman filtering; 7.15 Parameter estimation; 7.16 Applications; 7.17 Concluding remarks; Appendix 7.1: Fourier series representation of a periodic function; References; 8 Artificial neural networks; 8.1 Introduction; 8.2 Origin of artificial neural networks; 8.3 Unconstrained optimization techniques; 8.4 Perceptron; 8.5 Types of activation functions; 8.6 Types of artificial neural networks; 8.7 Learning modes and learning
8.8 BP algorithm8.9 ANN implementation details; 8.10 Feedback Systems; 8.11 Problems and limitations; 8.12 Application areas; 8.13 Concluding remarks; References; 9 Radial basis function (RBF) neural networks; 9.1 Introduction; 9.2 Interpolation; 9.3 Regularization; 9.4 Generalized RBFs; 9.5 Normalized radial basis functions (NRBFs) and kernel regression; 9.6 Learning of RBFs; 9.7 Curse of dimensionality; 9.8 Performance criteria; 9.9 Comparison of MLP versus RBF networks; 9.10 Applications; 9.11 Concluding remarks; References; 10 Fractals and chaos; 10.1 Introduction; 10.2 Fractal dimensions
Summary
Introduction. Historical Development of the Systems Theory Approach to Modelling. Population Dynamics. Reaction Kinetics. Water Quality Systems. Longitudinal Dispersion. Stochastic Models - Time Series Analysis and Modeling. Artificial Neural Networks. Radial Basis Function Networks. Fuzzy Logic Systems and Their Variations. Dynamical Systems Approach - Phase Space Re-Construction. Genetic Algorithms and Genetic Programming. Wavelet Decomposition. Process Based (Distributed) Models. Model Parameter Optimization. Closure
Bibliography
Includes bibliographical references and index
Notes
Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force. WlAbNL
