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Machine Learning Forecasts of Hourly and Daily Watertable Levels in a Wet Prairie.
  • Enrique Gomezdelcampo,
  • Priyanka More
Enrique Gomezdelcampo
Bowling Green State Univ

Corresponding Author:egomezd@bgsu.edu

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Priyanka More
South Carolina Department of Natural Resources
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Wet Prairies are unique communities occurring in isolated, saturated depressions that typically have standing water from late fall to late spring and then dry up and are burned by lightning or human-set fires in summer. Wet prairies are highly sensitive to precipitation and evapotranspiration patterns as they are highly dependent on shallow groundwater levels. Three years (May 2015-May 2018) of hourly watertable data recorded by a datalogger in a piezometer, and hourly precipitation, temperature, relative humidity, and wind speed data from the Toledo Express Airport (Ohio) weather station were used as inputs for a nonlinear autoregressive neural network with exogenous inputs (NARX) model. The data was used to train the model and to forecast watertable levels at an hourly and a daily time step. The NARX model used a Levenberg-Marquardt learning algorithm along with a combination of input delay, feedback delay, and hidden layers. The training set encompassed 70% of the total dataset, with cross-validation and testing covering the remaining 30% of the total dataset split equally among them. The model was trained with the hourly data and then the hourly data was aggregated to a daily record, and a new NARX model trained using this new time-step. The intention was to see if a much smaller data record, but typically all that is needed by a land manager, was capable of producing a satisfactory watertable forecast. The NARX model’s performance was assessed using R2, RMSE, and the Nash-Sutcliffe index. The NARX model provided successful short-term forecasts (6 months) for hourly and daily temporal resolutions. The R², RMSE, and Nash-Sutcliffe for the hourly testing period were 0.85, 0.08, and 0.85 respectively. For the daily testing period the R², RMSE, and Nash-Sutcliffe were 0.91, 0.07, and 90 respectively. The NARX model was not able to predict a sudden increase in water table levels due to a large snowmelt event in 2018, but this is not surprising as the model was not trained using snow melt events or a snow depth variable. Regardless of its current limitations, land managers could use this NARX model to better understand watertable patterns in wet prairies, one of the main drivers of this natural community.