Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting
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 i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/13/6/778 |
| _version_ | 1797541554759401472 |
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| author | Joanna Kajewska-Szkudlarek Wojciech Łyczko |
| author_facet | Joanna Kajewska-Szkudlarek Wojciech Łyczko |
| author_sort | Joanna Kajewska-Szkudlarek |
| collection | DOAJ |
| description | 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 r<sup>2</sup> 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. |
| first_indexed | 2024-03-10T13:17:36Z |
| format | Article |
| id | doaj.art-a16e704ceb2a4c819a77e0754a7896ae |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T13:17:36Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-a16e704ceb2a4c819a77e0754a7896ae2023-11-21T10:19:28ZengMDPI AGWater2073-44412021-03-0113677810.3390/w13060778Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series ForecastingJoanna Kajewska-Szkudlarek0Wojciech Łyczko1Institute of Environmental Engineering, Wrocław University of Environmental and Life Sciences, Grunwaldzki Square 24, 50-363 Wrocław, PolandInstitute of Environmental Protection and Development, Wrocław University of Environmental and Life Sciences, Grunwaldzki Square 24, 50-363 Wrocław, PolandEffective 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 r<sup>2</sup> 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.https://www.mdpi.com/2073-4441/13/6/778Hellwig methodinput variables selectiongroundwater level forecastingsupport vector regressiontime series reconstructionwetlands for wastewater treatment |
| spellingShingle | Joanna Kajewska-Szkudlarek Wojciech Łyczko Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting Water Hellwig method input variables selection groundwater level forecasting support vector regression time series reconstruction wetlands for wastewater treatment |
| title | Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting |
| title_full | Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting |
| title_fullStr | Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting |
| title_full_unstemmed | Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting |
| title_short | Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting |
| title_sort | assessment of hellwig method for predictors selection in groundwater level time series forecasting |
| topic | Hellwig method input variables selection groundwater level forecasting support vector regression time series reconstruction wetlands for wastewater treatment |
| url | https://www.mdpi.com/2073-4441/13/6/778 |
| work_keys_str_mv | AT joannakajewskaszkudlarek assessmentofhellwigmethodforpredictorsselectioningroundwaterleveltimeseriesforecasting AT wojciechłyczko assessmentofhellwigmethodforpredictorsselectioningroundwaterleveltimeseriesforecasting |