Performance Assessment of Global Optimization Algorithms in Automatic Calibration of Groundwater Models

Performance Assessment of Global Optimization Algorithms in Automatic Calibration of Groundwater Models

Sathyadevan, Sruthi (TU Delft Civil Engineering and Geosciences)

van Nooijen, Ronald (mentor)
van der Hauw, Koen (graduation committee)
van de Giesen, Nick (graduation committee)
Zaadnoordijk, Willem Jan (graduation committee)
Meulenbroek, Bernard (graduation committee)

Delft University of Technology

2018-10-12

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.

Groundwater
Optimisation
Calibration
Modelling
Model
Hydrology
water resources management
Water
groundwater modelling
Algorithm
Genetic Algorithm
Parameter Estimation

http://resolver.tudelft.nl/uuid:349c0a4c-56a0-4eb2-b8b1-e6c9bc62a13d

Student theses

master thesis

© 2018 Sruthi Sathyadevan