Soft Computing in Water Resources Engineering
WIT Press (Verlag)
978-1-84564-636-3 (ISBN)
). The ANN, FL, and GA are fairly new methods in water resources engineering. The first publications appeared in the early 1990s and quite a few studies followed in the early 2000s. Although these methods are currently widely known in journal publications, they are still very new for many scientific readers and they are totally new for students, especially undergraduates. Numerical methods were first taught at the graduate level but are now taught at the undergraduate level. There are already a few graduate courses developed on AI methods in engineering and included in the graduate curriculum of some universities. It is expected that these courses, too, will soon be taught at the undergraduate levels.
Prof. Dr. Gokmen TAYFUR is an associate professor in the Civil Engineeirng Department at the Izmir Institute of Technology where he teaches graduate course on numerical methods in engineering, artificial intelligence methods in engineering, hydrology and hydraulics, and non-point source pollution. He also teaches undergraduate courses on numerical methods and analysis in engineering, He received his Ph.D. and MS degrees from the University of California in Davis. And his undergraduate degree from Istanbul Technical University. His research interests include Surface and subsurface flows; Rainfall-runoff induced erosion/sediment transport , Solute transport in saturated and unsaturated zone; Solute transport by surface flows; and Application of artificial intelligence methods in water resources engineering and water quality. He has written numerous conference and journal papers and research reports on those topics.
PART I - ARTIFICIAL NEURAL NETWORKS Chapter 1 - Introduction to Artificial Neural Networks General View; Biological Neuron; Artificial Neuron; Artificial Neural Network; History; General Properties of ANN; Types; Architecture; Neuro-dynamics; ANN versus Other Models Chapter 2 - Artificial Neuron Components of Artificial Neuron; Methods for Computing Net Information; Summation (P) method; Maximum (max) method; Minimum (min) method; Product (Q) method; Activation Functions; Linear function; Step function; Rampage function; Gaussian function; Sigmoid function; Hyperbolic tangent function Chapter 3 - Network Training Pre-Training Procedures; Data Standardization; Standardization methods when using sigmoid function; Standardization methods when using hyperbolic tangent function; Network Initialization; Network Training; Back-propagation algorithm; Updating weights in output-inner layers; Updating weights in inner-input layers; Worked examples; Radial basis function; Conjugate gradient algorithm; Cascade correlation algorithm; Generalized regression algorithm; Learning Rules; Learning Parameter; Appendix; Exercise Problem Chapter 4 - Model Testing De-standardization of Model Output; Evaluating Model Performance; Over-training and Cross-training Chapter 5 - Model Application in Water Resources Engineering Prediction; Total suspended sediment; Seepage; Dispersion coefficient; Sheet sediment; Runoff at plot scale; Runoff at watershed scale; Flood hydrograph at basin scale; Classification; Forecasting; Forecasting flood hydrograph at basin scale; Extrapolation; Filling Gap in Time Series Data; References PART II - FUZZY LOGIC ALGORITHM Chapter 6 - Introduction to Fuzzy Logic Algorithm General View; Basic Concept in Fuzzy Logic; Fuzzy Systems Chapter 7 - Fuzzy Membership Functions, Set Operations, and Fuzzy Relations Fuzzy Membership Functions; Fuzzy Set Operations; Set representation; Set operations; Union of sets; Intersection of sets; Complementary sets; Subsets; Operation properties of fuzzy sets; Operations unique to fuzzy sets; Concentration; Dilation; Normalization; Intensification; Fuzzy Relations; Exercise Questions Chapter 8 - Constructing Fuzzy Model Fuzzification; Fuzzy Rule Base;Fuzzy Inference Engine; Inference sub-process; Composition sub-process; Defuzzification; Exercise Questions Chapter 9 - Fuzzy Model Application in Water Resources Engineering Introduction; TSS Prediction; Model development; Model calibration and application; Sheet Sediment Prediction; Fuzzy model; Physics-based model; ANN model; Peak Discharge Prediction; Experimental data; ANN model training and testing; FL model calibration and validation; KWA model calibration and validation; Runoff Hydrograph Simulation; ANN model training and testing; FL model calibration and validation; KWA model calibration and verification; Hydrograph Simulation at Watershed Scale; Dispersion Prediction; Experimental data; Regression-based model; Fuzzy model; References PART III - GENETIC ALGORITHMS Chapter 10 - Genetic Algorithms (GAS) Introduction; Basic Units of GA; GA Operations; Forming initial gene pool; Evaluating fitness of each chromosome; Selection; Cross-over operation; Single cut; Double cut; Multiple cut; Uniform crossing; Using sub-chromosome; Reversing; Mutation Chapter 11 - Variant of Genetic Algorithm Variant of Genetic Algorithms; Responsive perturbation algorithm; Trait-based heterogeneous populations (TbHP); Trait-based heterogeneous populations plus (TbHP+); Test Functions; Model Testing Chapter 12 - Genetic Algorithm Model Applications in Water Resources Engineering GA Application Problems; Longitudinal dispersion coefficient in natural streams; Hydrograph simulation; Watershed and hydrologic data; GA-RCM model implementation and calibration; Hydrograph predictions; Sensitivity analysis; Number of events used in calibration; Using shorter wave travel time eventsin the calibration; Using lower peak events in calibration; Hydrograph simulation using level data; Hydrograph predictions; Mean and bankfull discharge prediction; Non-linear regression method; Artificial neural networks method; Fuzzy method; Genetic algorithm; Appendix; References
| Erscheint lt. Verlag | 7.12.2011 |
|---|---|
| Zusatzinfo | Illustrations |
| Verlagsort | Southampton |
| Sprache | englisch |
| Maße | 155 x 230 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Office Programme |
| Mathematik / Informatik ► Mathematik | |
| ISBN-10 | 1-84564-636-3 / 1845646363 |
| ISBN-13 | 978-1-84564-636-3 / 9781845646363 |
| Zustand | Neuware |
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