Document Type


Publication Date



Surrogate modeling approach has been adopted in the study to replace computationally expensive physical-based numerical flow and transport model. Two approximate surrogate models namely, Artificial Neural Network (ANN) and Gaussian Process Model (GPM) are developed individually using a scenario database generated from the density dependent numerical flow and transport model OpenGeoSys (OGS). The state-space surrogates have the flexibility to move freely from one point to another within a time frame of decades and also to allow for moderate extrapolation in the case of extreme abstractions. The performance of the GPM was better in many cases with a little compromise on the computational time. A comparison of the performances of both the surrogates shows that they are reliable enough to be used in the management frameworks for decision making.


Session S5-01, Special Session: Computational Intelligence in Data Driven and Hybrid Models and Data Analysis I



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.