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Open AccessArticle

Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting

1
Institute of Environmental Engineering, Wrocław University of Environmental and Life Sciences, Grunwaldzki Square 24, 50-363 Wrocław, Poland
2
Institute of Environmental Protection and Development, Wrocław University of Environmental and Life Sciences, Grunwaldzki Square 24, 50-363 Wrocław, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Aldo Fiori
Water 2021, 13(6), 778; https://doi.org/10.3390/w13060778
Received: 11 February 2021 / Revised: 5 March 2021 / Accepted: 8 March 2021 / Published: 12 March 2021
(This article belongs to the Special Issue Water and Wastewater Management under a Climate Change)
Effective groundwater planning and management should be based on the prediction of available water volume. The complex nature of groundwater systems makes this complicated and requires the use of complex methods. Data-driven models using computational intelligence are becoming increasingly popular in that field. The key issue in predictive modelling is the selection of input variables. Wrocław-Osobowice irrigation fields were a wastewater treatment plant until 2013. The monitoring of groundwater levels is being continued to assess the water relations in that area after the end of their exploitation. The aim of the study was to assess the Hellwig method for predictors’ selection in groundwater level forecasting with support vector regression models. Data covered the daily time series of groundwater level in the period 2015–2019. Obtained models with a root mean squared error (RMSE) of 0.024–0.292 m and r2 of 0.7–0.9 were considered as high quality. Moreover, they showed good prediction ability for high as well as low groundwater values. Additionally, the proposed method is simple, and its implementation only requires access to groundwater level measurement data. It may be useful in groundwater management and planning in terms of actual climate change and threat of water deficits. View Full-Text
Keywords: Hellwig method; input variables selection; groundwater level forecasting; support vector regression; time series reconstruction; wetlands for wastewater treatment Hellwig method; input variables selection; groundwater level forecasting; support vector regression; time series reconstruction; wetlands for wastewater treatment
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MDPI and ACS Style

Kajewska-Szkudlarek, J.; Łyczko, W. Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting. Water 2021, 13, 778. https://doi.org/10.3390/w13060778

AMA Style

Kajewska-Szkudlarek J, Łyczko W. Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting. Water. 2021; 13(6):778. https://doi.org/10.3390/w13060778

Chicago/Turabian Style

Kajewska-Szkudlarek, Joanna; Łyczko, Wojciech. 2021. "Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting" Water 13, no. 6: 778. https://doi.org/10.3390/w13060778

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