Machine Learning Supported Groundwater Model Calibration with Modflow, Flopy, PySal and Scikit Learn - Tutorial
/We have done a tutorial on a low-level-complexity model with rivers, lakes, recharge and regional groundwater flow done in Model Muse in a previous tutorial. The model was imported as an object in Python with Flopy. A sensibility analysis was done with SALib to assess the response for the object model groundwater flow to a different sample of parameters and a resulting set of parameters and corresponding heads (parameters -> heads) were recorded. Then a machine learning regression was performed with Scikit-Learn with the inverse set (heads->parameters) to get the predicted parameters for the observed data. Different error measurements were performed for two model cases to assess the overall quality of the neural network regressor.
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FloPy is the Python library that builds and executes MODFLOW models; this library has been enhanced to provide full support of MODFLOW 6 with most of its recent development is related to functionality for MODFLOW 6, tools to use vector and raster spatial data and common plotting and export functionality.