Machine‐Learning Methods for Water Table Depth Prediction in Seasonal Freezing‐Thawing Areas

Long‐term and accurate predictions of regional groundwater hydrology are important for maintaining environmental sustainability in arid agricultural areas that experience seasonal freezing and thawing where serious water‐saving measurements are used. In this study, we firstly developed a machine‐learning method by integrating a multivariate time series controlled auto‐regressive method and the ridge regression method (CAR‐RR) for water table depth modeling. We applied and evaluated this model in the Hetao Irrigation District, located in northwest China where the freezing‐thawing period is 5 months long. To train and validate the model, we used monthly data of water diversion, precipitation, evaporation, and drainage from 1995 to 2013. The CAR‐RR model yielded more accurate results than the support vector regression (SVR) and multiple linear regression (MLR) models did in the validation period. To extend the model applicability during freezing‐thawing periods, we included additional temperature information. We compared results obtained using temperature only during the freezing‐thawing period with results obtained without temperature, which showed that the input data of the temperature during the freezing‐thawing period significantly improved the model accuracy. To resolve the problem of capturing the peaks and troughs of CAR‐RR, we further developed an integrated CAR‐SVR model to consider the nonlinearity. The optimal model (CAR‐SVR) was then used to predict the water table depth under future water‐saving measurements. It demonstrated that water diversion was the most important factor affecting the water table depth. A water table depth with less than 3.64 billion m3 water diversion will result in risks of environment problems. Article impact statement: Water table depth prediction in seasonal freezing–thawing area, using integrated machine‐learning methods with and without temperature data.