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Modelling for Coastal Hydraulics and Engineering

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Modelling for Coastal Hydraulics and Engineering Modelling for Coastal Hydraulics and Engineering Modelling for Coastal Hydraulics and Engineering K.W. Chau First published 2010 by Spon Press 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN Simultaneously published in the USA and Canada by Spon Press 270 Madison Avenue, New York, NY 10016, USA This edition published in the Taylor & Francis e-Library, 2010. To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to www.eBookstore.tandf.co.uk. Spon Press is an imprint of the Taylor & Francis Group, an informa business c 2010 K.W. Chau All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. This publication presents material of a broad scope and applicability. Despite stringent efforts by all concerned in the publishing process, some typographical or editorial errors may occur, and readers are encouraged to bring these to our attention where they represent errors of substance. The publisher and author disclaim any liability, in whole or in part, arising from information contained in this publication. The reader is urged to consult with an appropriate licensed professional prior to taking any action or making any interpretation that is within the realm of a licensed professional practice. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data Modelling for coastal hydraulics and engineering / [edited by] K.W. Chau. p. cm. Includes bibliographical references and index. 1. Coastal engineering--Mathematics. 2. Coastal engineering--Data processing. 3. Hydraulic engineering--Mathematics. 4. Coasts--Computer simulation. 5. Coast changes--Mathematical models. I. Chau, K. W. (Kwok Wing) TC209.M58 2010 627.580151--dc22 2009027718 ISBN 0-203-88476-0 Master e-book ISBN ISBN 10: 0-415-48254-2 (hbk) ISBN 10: 0-203-88476-0 (ebk) ISBN 13: 978-0-415-48254-7 (hbk) ISBN 13: 978-0-203-88476-8 (ebk) Contents 1 Introduction 1 2 Coastal modelling 3 2.1 Introduction 3 2.2 Hydrodynamic modelling 3 2.3 Water quality modelling 6 2.4 Governing equations 6 2.5 Conclusions 7 3 Conventional modelling techniques for coastal engineering 8 3.1 Introduction 8 3.2 Mechanistic modelling 8 3.2.1 Model manipulation 9 3.2.2 Generations of modelling 9 3.2.3 Incorporation of artificial intelligence (AI) into modelling 10 3.3 Temporal and spatial discretizations 10 3.4 Conclusions 17 4 Finite difference methods 18 4.1 Introduction 18 4.2 Basic philosophy 18 4.3 One-dimensional models 19 4.4 Two-dimensional models 20 4.4.1 2-D depth-integrated models 21 4.4.2 2-D lateral-integrated models 22 4.5 Three-dimensional models 22 4.6 A 3-D hydrodynamic and pollutant transport model 23 4.6.1 Hydrodynamic equations 25 4.6.2 Pollutant transport equation 30 4.7 Advantages and disadvantages 32 4.8 Applications and case studies 33 4.8.1 Description of the Pearl River estuary 34 4.8.2 Boundary and initial conditions 35 vi Contents 4.8.3 Calibrations 40 4.8.4 Simulated results 48 4.9 Conclusions 51 5 Finite element methods 53 5.1 Introduction 53 5.2 Basic philosophy 53 5.3 One-dimensional models 54 5.4 Two-dimensional models 55 5.4.1 2-D depth-integrated models 55 5.4.2 2-D lateral-integrated models 56 5.5 Three-dimensional models 57 5.6 Characteristic-Galerkin method 58 5.6.1 Formulation of the discretized equations 58 5.6.2 Two-step algorithm 61 5.6.3 A characteristics-based approach 62 5.6.4 The conservative hydrodynamic and mass transport equations 64 5.6.5 Accuracy analysis of advection-dominated problems 66 5.7 Verification of the numerical scheme 68 5.7.1 Pure advection of a Gaussian hill 69 5.7.2 Pure rotation of a Gaussian hill 70 5.7.3 Advective diffusion in a plane shear flow 71 5.7.4 Continuous source in a tidal flow 73 5.7.5 Long wave in a rectangular channel with quadratic bottom bathymetry 74 5.8 Advantages and disadvantages 76 5.9 Prototype application I: mariculture management 77 5.9.1 General description of Tolo Harbour 77 5.9.2 Dynamic steady-state simulation: M2 tidal forcing 79 5.9.3 Real tide simulation for seven days (42 tidal constituents) 81 5.10 Prototype application II: the effect of reclamation on tidal current 83 5.10.1 General description of Victoria Harbour 83 5.10.2 Hydrodynamic simulation for an M2 tidal forcing 83 5.10.3 Real tide simulation for four principal tidal constituents 86 5.10.4 Effect of reclamation 86 5.11 Conclusions 89 6 Soft computing techniques 91 6.1 Introduction 91 6.2 Soft computing 93 Contents vii 6.3 Data-driven machine learning (ML) algorithms 97 6.4 Knowledge-based expert systems 105 6.5 Manipulation of conventional models 107 6.6 Conclusions 109 7 Artificial neural networks 110 7.1 Introduction 110 7.2 Supervised learning algorithm 110 7.3 Backpropagation neural networks 113 7.4 Advantages and disadvantages of artificial neural networks 116 7.5 Prototype application I: algal bloom prediction 117 7.5.1 Description of the study site 117 7.5.2 Criterion of model performance 119 7.5.3 Model inputs and output 120 7.5.4 Significant input variables 120 7.5.5 Results and discussion 125 7.6 Prototype application II: long-term prediction of discharges 127 7.6.1 Scaled conjugate gradient (SCG) algorithm 127 7.6.2 Prediction of discharges in Manwan hydropower station 128 7.6.3 Results and discussion 129 7.7 Conclusions 131 8 Fuzzy inference systems 133 8.1 Introduction 133 8.2 Fuzzy logic 133 8.3 Fuzzy inference systems 136 8.4 Adaptive-network-based fuzzy inference system (ANFIS) 138 8.4.1 ANFIS architecture 139 8.4.2 Hybrid learning algorithm 142 8.5 Advantages and disadvantages of fuzzy inference systems 143 8.6 Applications and case studies 143 8.6.1 Model development and testing 144 8.6.2 Results and discussion 145 8.6.3 Result comparison with an ANN model 147 8.7 Conclusions 148 9 Evolutionary algorithms 150 9.1 Introduction 150 9.2 Genetic algorithms (GA) 150 9.3 Genetic programming (GP) 153 9.4 Particle swarm optimization (PSO) 154 9.5 Advantages and disadvantages of evolutionary algorithms 156 viii Contents 9.6 Prototype application I: algal bloom prediction by GP 156 9.6.1 Description of the study site 157 9.6.2 Criterion of model performance 158 9.6.3 Model inputs and output 158 9.6.4 Significant input variables 159 9.6.5 Results and discussion 163 9.7 Prototype application II: flood forecasting in river by ANN-GA 166 9.7.1 Algorithm of ANN-GA flood forecasting model 166 9.7.2 The study site and data 167 9.7.3 Results and discussion 170 9.8 Prototype application III: water stage forecasting by PSO-based ANN 174 9.8.1 The study site and data 174 9.8.2 Results and discussion 175 9.9 Conclusions 176 10 Knowledge-based systems 178 10.1 Introduction 178 10.2 Knowledge-based systems 178 10.2.1 Components of knowledge-based systems 179 10.2.2 Characteristics of knowledge-based systems 181 10.2.3 Comparisons with conventional programs 181 10.2.4 Development process of knowledge-based systems 182 10.2.5 Development tools for knowledge-based systems 183 10.2.6 Knowledge representation 185 10.3 Rule-based expert systems 186 10.3.1 Problem-solving strategy 186 10.4 Blackboard architecture 187 10.5 Advantages and disadvantages of knowledge-based systems 190 10.5.1 Advantages of knowledge-based systems 190 10.5.2 Drawbacks of knowledge-based systems 191 10.6 Applications and case studies 192 10.6.1 COASTAL_WATER 195 10.6.2 ONTOLOGY_KMS 199 10.7 Conclusions 204 11 Conclusions 205 References 208 Index 226 1 Introduction In the analysis of the coastal water process, numerical models are often employed to simulate flow and water quality problems. The rapid develop- ment of computing technology has furnished a large number of models to be employed in engineering or environmental problems. To date, a variety of coastal models are available, and the modelling techniques have become quite mature. The numerical technique can be based on the finite element method (Kliem et al. 2006; Jones and Davies 2007), finite difference method (Buonaiuto and Bokuniewicz 2008; Tang et al. 2009), boundary element method (Karamperidou et al. 2007; Duan et al. 2009), finite volume method (Aoki and Isobe 2007; Qi et al. 2009), or Eulerian-Lagrangian method (Cheng et al. 1984). The time-stepping algorithm can be implicit (Holly and Preissmann 1977), semi-implicit (Ataie-Ashtiani and Farhadi 2006), explicit (Ghostine et al. 2008), or characteristic-based (Ataie-Ashtiani 2007; Perera et al. 2008). The shape function can be of the first order, second order, or a higher order. The modelling can be simplified into different spatial dimen- sions, i.e. a one-dimensional model (Chau and Lee 1991a; Abderrezzak and Paquier 2009), two-dimensional depth-integrated model (Leendertse 1967; Tang et al. 2009), two-dimensional lateral-integrated model (Wu et al.
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