Groundwater level prediction using a SOM-aided stepwise cluster inference model

Accurate groundwater level (GWL) prediction can contribute to sustaining reliable water supply to domestic, agricultural and industrial uses as well as ecological services, especially in arid and semi-arid areas. In this paper, a regional GWL modeling framework was first presented through coupling both spatial and temporal clustering techniques. Specifically, the self-organizing map (SOM) was applied to identify spatially homogeneous clusters of GWL piezometers, while GWL time series forecasting was performed through developing a stepwise cluster multisite inference model with various predictors including climate conditions, well extractions, surface runoffs, reservoir operations and GWL measurements at previous steps. The proposed modeling approach was then demonstrated by a case of an arid irrigation district in the western Hexi Corridor, northwest China. Spatial clustering analysis identified 6 regionally representative central piezometers out of 30, for which sensitivity and uncertainty analysis were carried out regarding GWL predictions. As the stepwise cluster tree provided uncertain predictions, we added an AR(1) error model to the mean prediction to forecast GWL 1 month ahead. Model performance indicators suggest that the modeling system is a useful tool to aid decision-making for informed groundwater resource management in arid areas, and would have a great potential to extend its applications to more areas or regions in the future. •We propose a coupled clustering approach to support groundwater level predictions.•The spatial clusters of regional groundwater piezometers are derived through SOM.•Stepwise cluster inference models are developed to perform multisite predictions.•The approach is well demonstrated in an irrigation area of the China Hexi Corridor.•Such post-processor as the AR error model helps to improve the prediction accuracy.