Using Remote Sensing and Machine Learning to Estimate Groundwater Use in
the Mississippi Alluvial Plain
Abstract
In this study, we improve estimates of groundwater usage across the
Mississippi Alluvial Plain (MAP) in support of an ongoing USGS effort to
model the groundwater resources of the region. Previously, the USGS
developed a lookup table based on flowmeter data that estimates water
use based on average water use for each crop type, for specific regions
and precipitation amounts. The latest iteration of this model is known
as Aquaculture and Irrigation Water-Use Model (AIWUM) 1.1 and we refer
to our method as AIWUM 2.0. Here, we apply gradient boosted trees (GBT)
to predict groundwater use across the MAP from 2014-2019. The predictor
variables include well locations (latitude and longitude), crop type,
precipitation, maximum temperature (the average daily maximum from April
- September), total evapotranspiration estimated with MOD16, and surface
run-off (TerraClimate). The existing flowmeter data over the Mississippi
Delta were randomly split into training (80%) and validation (20%)
data. The following parameters in the GBT algorithm were tuned: the
number of estimators, learning rate, maximum tree depth, the objective
function, and the percentage of training data that are randomly sampled
in the training process. We observe very low model overfitting where the
training error metrics are R2 = 0.58, mean absolute
error (MAE) = 0.30 ft, and root mean square error (RMSE) = 0.51 ft,
respectively, and the corresponding test metrics are
R2 = 0.49, MAE = 0.32 ft, and RMSE = 0.51 ft. This is
an improvement over AIWUM 1.1, where the corresponding
R2, MAE, and RMSE were 0.27, 0.40 ft, and 0.67 ft.
These water use estimates will result in an improved ability to
accurately model groundwater flow in this aquifer, which accounts for
roughly 20% of the total groundwater pumping in the United States.