Wavelet-based de-noising in groundwater quality and quantity prediction by an artificial neural network
| Autor: | Farnaz Daneshvar Vousoughi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Water Supply, Vol 23, Iss 3, Pp 1333-1348 (2023) |
| Druh dokumentu: | article |
| ISSN: | 1606-9749 1607-0798 |
| DOI: | 10.2166/ws.2023.021 |
| Popis: | The present study uses a wavelet-based clustering technique to identify spatially homogeneous clusters of groundwater quantity and quality data and to select the most effective input data for the feed-forward neural network (FFNN) model to predict the groundwater level (GL), pH and HCO3− in groundwater. In the second stage of this methodology, first, GL, pH and HCO3− time series of different piezometers were de-noised using a threshold-based wavelet method and the impact of de-noised and noisy data were compared in temporal GL, pH and HCO3− modeling by the artificial neural network (ANN). The results suggest that the proposed model decreases the dimensionality of the input layer and consequently the complexity of the FFNN model with acceptable efficiency in the spatiotemporal simulation of GL and groundwater quality parameters. Also, the application of wavelet-based de-noising for modeling GL, pH and HCO3− parameters with ANN increases the accuracy of predictions, respectively, up to 11.53, 11.94 and 38.85% on average. HIGHLIGHTS Wavelet-based de-noising was employed as the data pre-processing method used for groundwater level modeling.; Wavelet-based de-noising was used for pH and modeling.; The wavelet transform coherence test was used for clustering the groundwater level.; Using a de-noising approach led to a more precise prediction of groundwater quality and quantity.; Using clustering led to improving the accuracy of modeling.; |
| Databáze: | Directory of Open Access Journals |
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