A novel hybrid mechanistic-data-driven model identification framework using NSGA-II
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
This paper describes a novel evolutionary data-driven model (DDM) identification framework using the NSGA-II multi-objective genetic algorithm. The central concept of this paper is the employment of evolutionary computation to search for model structures among a catalog of models, while honoring the physical principles and the constitutive theories commonly used to represent the system/processes being modeled. The presented framework provides high computational efficiency through connecting a series of NSGA-II runs which share results. Furthermore, the employment of a multi-objective optimization algorithm enables a unique way of incorporating different aspects of model goodness in the model selection process, and also, at the end of the search procedure, provides a number of potential optimal model structures, making it possible for the modeler to make a choice based on the goal of the modeling. As an illustration, the framework is used for modeling wash-off and build-up of suspended solids (TSS) in highway runoff. The performance of the discovered model confirms the potential of the proposed evolutionary DDM framework for modeling environmental processes.
|Number of pages||19|
|Journal||Journal of Hydroinformatics|
|Publication status||Published - 2012|
- 1st flush, data-driven modeling, design, evolutionary computation, evolutionary multiobjective optimization, genetic algorithm, genetic algorithms, nsga-ii, regression, runoff, symbolic regression