SHALLOW GROUNDWATER LEVELS IN A WET PRAIRIE: USING MACHINE LEARNING TO PREDICT WATER LEVEL CHANGES IN NORTHWEST OHIO

The Oak Openings Region (OOR) of Northwest Ohio is well known for rare plant and animal species. It contains the few remaining wet prairie ecosystems in the area. Wet prairie ecosystems are highly sensitive to precipitation patterns and therefore to changing climate. Shallow groundwater levels at the study site are greatly influenced by precipitation and evapotranspiration. The rate of evapotranspiration varies with air temperature and other weather factors. A study at a small watershed in the Oak Openings was conducted to determine the relationship between shallow groundwater level (water table) and precipitation and evapotranspiration. Hourly groundwater level data were collected using data loggers installed at six different piezometers within the study area from May 2015 to November 2017. Hourly precipitation and temperature data was obtained from the Toledo Express Airport weather station (TOL), located about one mile from the study area. A time series analysis of the water table and precipitation and evapotranspiration was performed. Pearson correlation tests showed a negative correlation with evapotranspiration (r = -0.46). A lagged cross correlation analysis of the water table level and precipitation time series data, estimated a maximum value of r of 0.24, 0.71, and 0.31 for the whole dataset, for rain events, and for the dry period between two rain events, respectively. These results varied with different seasons, thereby showing a seasonality effect on the correlation between precipitation, evapotranspiration, and water levels. Based on the time series analysis and using a multilayer feed-forward neural network (ANN), a good prediction was obtained between precipitation, evapotranspiration and water table levels for long-term time periods. This ANN model will be used to predict possible changes to the wet prairie hydrology with expected climate change in the Midwest.