Sruthi Sathyadevan Performance Assessment of Global Optimization Algorithms in Automatic Calibration of Groundwater Models 2 Performance Assessment of Global Optimization Algorithms in Automatic Calibration of Groundwater Models By Sruthi Sathyadevan in partial fulfilment of the requirements for the degree of Master of Science in Civil Engineering at the Delft University of Technology, to be defended publicly on Friday October 12, 2018 at 04:00 PM. Supervisor: Prof. dr. ir. Ronald van Nooyen, TU Delft Thesis committee: Prof. dr. ir. Nick van de Giesen, TU Delft Dr. ir. Willem-Jan Zaadnoordijk, TU Delft/TNO Ir. Bernard J. Meulenbreuk, TU Delft An electronic version of this thesis is available at http://repository.tudelft.nl/. 3 4 Preface This report represents the culmination of my master thesis research that was undertaken as the final stage of my study Water Management at Delft University of Technology. I acquired a great deal of knowledge in groundwater flow modelling and on ways to efficiently do a scientific research. This could not have been made possible without the help and support of many. First, I would like to thank my university supervisor Ronald van Nooyen for his continued involvement at all stages of my thesis and ensuring his support when needed. A special thanks to my company supervisor Koen van der Hauw at Sweco Nederland for his immense patience and abundant knowledge I feel honoured to have been able to learn from. Further I thank my graduation committee for their feedback and guidance during my thesis. Heartfelt thanks go to Willem-Jan Zaadnoordijk, Bernard J. Meulenbreuk and Nick van de Giesen for steering my research in the right direction at all times. Without their support and advice, this report could not have been completed. Further, I want to thank Heleen Westerhof-Graafstal at the Waterschap De Dommel for her continued support during my research by passing on her knowledge. Last but not the least, I would like to thank my family and friends for their presence and support in my life without whom this thesis could not have been completed. Sruthi Sathyadevan Delft, October 2018 5 6 Contents Abstract ........................................................................................................................................................ 8 List of Figures ............................................................................................................................................... 9 List of Tables .............................................................................................................................................. 11 1. Introduction ..................................................................................................................................................................................................... 12 1.1. Groundwater modelling .................................................................................................................... 12 1.2. Need for parameter estimation ......................................................................................................... 19 1.3. Objective and research questions .................................................................................................... 21 2. Choice of Algorithms ................................................................................................................................................................................... 22 2.1. iPEST ............................................................................................................................................... 22 2.2. Genetic Algorithms ........................................................................................................................... 23 3. Study area ....................................................................................................................................................................................................... 29 3.1. Why this area? ................................................................................................................................. 32 3.2. Model Area ....................................................................................................................................... 33 3.3. Properties of the area ....................................................................................................................... 35 3.4. Expected outcomes .......................................................................................................................... 37 4. Methodology ................................................................................................................................................................................................. 38 4.1. Overview .......................................................................................................................................... 38 4.2. iMOD Groundwater Model ................................................................................................................ 38 4.3. Calibration concepts ......................................................................................................................... 42 4.4. Calibration with iPEST...................................................................................................................... 43 4.5. Calibration with Genetic Algorithm ................................................................................................... 45 4.6. Comparison strategy ........................................................................................................................ 48 5. Results .............................................................................................................................................................................................................. 49 5.1. iMOD Modeling ................................................................................................................................ 49 5.2. Final iMOD model v/s Triwaco Flairs model ..................................................................................... 57 5.3. Calibration using iPEST ................................................................................................................... 58 5.4. Calibration using GA ........................................................................................................................ 62 5.5. Comparison GA vs iPEST calibration output .................................................................................... 64 5.6. Final iMOD models ........................................................................................................................... 72 5.7. Validation ......................................................................................................................................... 75 5.8. GA modifications .............................................................................................................................. 77 6. Conclusions .................................................................................................................................................................................................... 80 6.1. Comparison of GA vs iPEST ............................................................................................................ 80 6.2. Performance factors ......................................................................................................................... 80 6.3. Physical plausibility of the results ..................................................................................................... 80 7. Recommendations ....................................................................................................................................................................................... 82 8. Bibliography ..................................................................................................................................................................................................... 83 9. Annex ................................................................................................................................................ 85 7 Abstract Calibration of groundwater models can be done manually by comparing the measured and computed groundwater heads or automatically using algorithms which do the work for you. Modules for this purpose are included in or linked externally to traditional groundwater modelling software and used. One such module PEST (Parameter ESTimation) uses the Levenburg Marquardt algorithm which is a combination of Gauss Newton and Gradient Descent methods to find the minima of a function. However, PEST is capable of finding only a local minimum for the objective function. A possibility exists that there is a global minimum that better fits our function and could give even better results for the optimization problem in, in our case, parameter estimation in groundwater models. A performance assessment has been done with a Genetic algorithm on a groundwater modelling problem in this study. 8 List of Figures Fig. 1.1. Modelling methodology (G.H.P. Oude Essink, 2000) Fig. 1.2. Different types of boundaries. Line ABC & EFG represent Dirichlet & Cauchy Boundaries, Lines HI & AD, Neumann Boundaries Fig. 1.3. Determination of steady state groundwater flow equation (Oude Essink, 2009) Fig. 1.4. Cell designations and boundary conditions