A Novel Approach to Uncertainty Quantification in Groundwater Table Modeling

Abbaszadeh Shahri, A., Shan, C. and Larsson, S. (2022) A Novel Approach to Uncertainty Quantification in Groundwater Table Modeling by Automated Predictive Deep Learning. Natural Resources Research 31(3): 1351-1373. doi.org/10.1007/s11053-022-10051-w.

Uncertainty quantification (UQ) is an important benchmark to assess the performance of artificial intelligence (AI) and particularly deep learning ensembled-based models. To overcome and address limitations, a novel ensemble, an automated random deactivating connective weights approach (ARDCW), is presented and applied to retrieved geographical locations of GWT data from a geo-engineering project in Stockholm, Sweden.