Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/138678
Title: DATA DRIVEN MODELLING AND KNOWLEDGE DISCOVERY IN WATER RESOURCES ENGINEERING
Authors: JAYASHREE CHADALAWADA
ORCID iD:   orcid.org/0000-0003-3224-1186
Keywords: Genetic programming, Model Induction, Lumped Conceptual Models, Evolutionary Flexible Modelling, Process based modelling, Unifying Hydrological Theory
Issue Date: 24-Aug-2017
Citation: JAYASHREE CHADALAWADA (2017-08-24). DATA DRIVEN MODELLING AND KNOWLEDGE DISCOVERY IN WATER RESOURCES ENGINEERING. ScholarBank@NUS Repository.
Abstract: Data driven approaches have the potential of becoming highly useful knowledge discovery tools when domain knowledge is incorporated into learning procedure. This research defines a Genetic Programming (GP) based conceptual modelling framework coded in an open source R environment to understand catchment scale hydrological processes. The state of the art applications of GP in hydrology involve the use of GP as a short-term prediction and forecast tool rather than as a modelling framework. In this study, GP simultaneously evolves suitable model structures and associated parameters in a readily interpretable form, that explain set of observations with the help of background knowledge. Thus evolved GP model configurations are found to be in good agreement with fieldwork evidence.
URI: http://scholarbank.nus.edu.sg/handle/10635/138678
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
ChadalawadaJayashree_ETD.pdf3.58 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

44
checked on Oct 11, 2018

Download(s)

39
checked on Oct 11, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.