Statistical predictions of groundwater levels and related spatial diagnostics for the Mississippi River Valley alluvial aquifer from the mmlMRVAgen1 statistical machine-learning software, GeoTIFF formatted

A multiple machine-learning model (Asquith and Killian, 2024) implementing Cubist and Random Forest regressions was used to predict monthly mean groundwater levels through time for the available years described in the metadata for the Mississippi River Valley alluvial aquifer (MRVA). The MRVA is the surficial aquifer of the Mississippi Alluvial Plain (MAP), located in the south-central United States. Employing two machine-learning techniques offered the opportunity to generate model and statistical error and covariance between them to estimate total uncertainty. Potentiometric surface predictions were made at the 1-kilometer grid scale using the National Hydrogeologic Grid (Clark and others, 2018). Results produced by the mmlMRVAgen1 software have been condensed into thirteen themes each containing a multi-banded GeoTIFF raster with 504 layers, corresponding to each month for the available years described in the metadata.
The themes include the final predicted monthly water-level altitudes, in feet North American Vertical Datum of 1988 (NAVD88), for the study area (pol), which were computed by pooling through weighted-mean averaging by cell the even and odd year predictions for that month. The depth to water was predicted in feet (nhgd2w), utilizing the NHG cell altitude as the land surface datum. Model errors were evaluated using both the normal error (modnorerr) in standard deviations of feet and the polynomial-density-quantile4 distribution (PDQ4)-error model (modpdqerr) without the inclusion of land-surface variation of the NHG. The equivalent standard deviations of these error models were calculated both with and without the inclusion of land-surface variation of the NHG (norerr, norerrnhg, pdqerr, pdqerrnhg). The lower and upper bounds (in feet) of the 90-percent prediction limits for both model error forms were computed (norlwr, norupr, pdqlwr, pdqupr). Lastly, the ratio of model error to total error (modtotrat) was also computed. Complementing each of the GeoTIFFs are `.json` extensions to each file. These provide additional multi-band support information. This double-file representation stems from the native GeoTIFF drivers within the terra R package underpinning the operations. Overall, the model objects created by the mmlMRVAgen1 from about 156,000 water-level records for about 58,000 wells report (1) a normalized Nash−Sutcliffe Efficiency (NNSE) of about 0.997, (2) a root-mean-square error (RMSE) of about 4.15 feet, and (3) a bias prior to computing the NNSE and RMSE of about 0.0963 feet before its subsequent removal (see mmlMRVAgen1 software diagnostics associated with "MRVA_MML_CONSTANTS"). The model objects also report for the 156,000 water-level records (1) a mean percent ratio of model error to total error of about 69.2 percent and (2) a mean width of about 12.05 feet for the 90-percent prediction bounds from the PDQ4 error framework (see mmlMRVAgen1 software diagnostics associated with "genMML/03step.R"). The model objects were used in post-model creation to predict each of the rasters provided in this data release. (Note, the results herein are associated with the "April 21, 2024" model run, see mmlMRVAgen1/model_archive/README.md.)
For a full description of covariate assemblage and hydrograph modeling, see Asquith and Killian (2022) (covMRVAgen1 software). For a full description of multiple machine-learning modeling, see Asquith and Killian (2024) (mmlMRVAgen1 software).

Tags: aquifer, arkansas, cubist, environment, geoscientificinformation, groundwater, groundwater-level, illinois, louisiana, machine-learning, mathematical-modeling, mississippi, mississippi-alluvial-plain, mississippi-river-valley-alluvial-aquifer, missouri, mrva, random-forest, tennessee, u-s-geological-survey, usgs-65f30833d34e9853bbf0cb2b, water-availability-and-use-science-program, wausp
Organization: Department of the Interior