Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models

Khali Bahaa , Broda Stefan , Adamowski Jan , Bogdan Ozga-Zieliński , Donohoe Amanda


Several groundwater-level forecasting studies have shown that data-driven models are simpler, faster to develop, and provide more accurate and precise results than physical or numerical-based models. Five data-driven models were examined for the forecasting of groundwater levels as a result of recharge via tailings from an abandoned mine in Quebec, Canada, for lead times of 1 day, 1 week and 1 month. The five models are: a multiple linear regression (MLR); an artificial neural network (ANN); two models that are based on de-noising the model predictors using the wavelet-transform (W-MLR, W-ANN); and a W-ensemble ANN (W-ENN) model. The tailing recharge, total precipitation, and mean air temperature were used as predictors. The ANN models performed better than the MLR models, and both MLR and ANN models performed significantly better after denoising the predictors using wavelet-transforms. Overall, the W-ENN model performed best for each of the three lead times. These results highlight the ability of wavelettransforms to decompose non-stationary data into discrete wavelet-components, highlighting cyclic patterns and trends in the time-series at varying temporal scales, rendering the data readily usable in forecasting. The good performance of the W-ENN model highlights the usefulness of ensemble modeling, which ensures model robustness along with improved reliability by reducing variance.
Author Khali Bahaa
Khali Bahaa,,
, Broda Stefan
Broda Stefan,,
, Adamowski Jan
Adamowski Jan,,
, Bogdan Ozga-Zieliński (FEE / CEP)
Bogdan Ozga-Zieliński,,
- Chair of Environmental Protection
, Donohoe Amanda
Donohoe Amanda,,
Journal seriesHydrogeology Journal, ISSN 1431-2174, (A 35 pkt)
Issue year2015
Keywords in EnglishGroundwater levels, Forecasting, Groundwater recharge, Ensemble modeling, Canada
Languageen angielski
Ozga-Zieliński..pdf 4.13 MB
Score (nominal)35
ScoreMinisterial score = 35.0, 28-11-2017, ArticleFromJournal
Ministerial score (2013-2016) = 35.0, 28-11-2017, ArticleFromJournal
Publication indicators WoS Impact Factor: 2015 = 2.028 (2) - 2015=2.386 (5)
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