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.
Schütze, Niels and Roy, Tirthankar, "Fast Neural Network Surrogates For Complex Groundwater Flow Models" (2014). CUNY Academic Works.
Session S5-01, Special Session: Computational Intelligence in Data Driven and Hybrid Models and Data Analysis I