دانلود کتاب Environmental and Hydrological Systems Modelling

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Content: 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 2.10 Challenges in hydrological modelling2.11 Concluding remarks
References
3 Population dynamics
3.1 Introduction
3.2 Malthusian growth model
3.3 Verhulst growth model
3.4 Predator-prey (Lotka-Volterra) model
3.5 Gompertz curve
3.6 Logistic map
3.7 Cell growth
3.8 Bacterial growth
3.9 Radioactive decay and carbon dating
3.10 Concluding remarks
References
4 Reaction kinetics
4.1 Introduction
4.2 Michaelis-Menten equation
4.3 Monod equation
4.4 Concluding remarks
References
5 Water quality systems
5.1 Dissolved oxygen systems 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