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|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)|
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