Reliable long-term groundwater level (GWL) prediction is essential to assess the availability of resources and the risk to drinking water supply in changing climatic and socio-economic conditions, especially in areas with water deficits. The modern approach in this area involves the use of machine learning methods. However, the greatest challenge in these methods lies in the optimization of input selection. The presented research concerns the selection of the best combination of predictors using the Hellwig method. It served as a preprocessing technique before GWL prediction using support vector regression (SVR) and multilayer perceptron (MLP) for three wells in the Greater Poland Province, where the largest water deficits occur, in the period 1975–2014. The results of this method were compared with those of the regression method, general regression model. For the case study under investigation, the Hellwig method found GWL at lags of −1 and −2 months, all precipitation from the current month, and delayed by −1 to −6 months, and past temperature at months −1, −3, −4 and −6 as the most informative input set. Such input led to a model accuracy of 0.003–0.022 for a mean squared error and r2 of >0.8. The results obtained with SVR were slightly better than those with MLP. Moreover, every well required an individual set of predictors, and additional meteorological inputs improved the models’ performance.

  • The performance of the Hellwig method in the selection of input variables for groundwater level (GWL) forecasting using support vector regression and multilayer perceptron.

  • GWL dependence on meteorological and hydrological conditions within the past 6 months.

  • Additional meteorological inputs can improve the performance of forecasts.

  • Neighbouring wells require an individual set of predictors.

  • GWL modelling in areas threatened by water scarcity.

Graphical Abstract

Graphical Abstract
Graphical Abstract
This content is only available as a PDF.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).