Bahmani, Ramin et Ouarda, Taha B. M. J. 
ORCID: https://orcid.org/0000-0002-0969-063X
(2021).
Groundwater level modeling with hybrid artificial intelligence techniques.
Journal of Hydrology
, vol. 595
.
p. 125659.
DOI: 10.1016/j.jhydrol.2020.125659.
Résumé
It is necessary to properly simulate groundwater levels in order to ensure an adequate management of scarce water resources. However, simulating groundwater levels accurately is one of the challenging issues in hydrology because of its complex system. In the current study, Gene Expression Programming (GEP) and M5 model tree (M5) are combined with Ensemble Empirical Mode Decomposition (EEMD) and Complementary Ensemble Empirical Mode Decomposition (CEEMD) methods for pre-processing input data to produce hybrid models for groundwater simulation. The performance of hybrid models is compared with the outputs of sole GEP and M5 and their counterparts combined with Wavelet transform (WT). The results indicate that pre-processing can improve the performance of the simple models and WT as well as CEEMD are better than EEMD to produce hybrid models. By employing pre-processing techniques, the improvement of R2 for GEP and M5, regarding Multiple Linear Regression (MLR) as a benchmark, is 13.11%, 6.65% and 8.20% for EEMD-GEP, CEEMD-GEP and CEEMD-M5 respectively, while it is –3.28% for EEMD-M5 for the first data set. For the second data set, the improvement of EEMD-GEP = 103.23%, CEEMD-GEP = 125.81% and CEEMD-M5 = 77.42%, and for the third data set, EEMD-GEP = 76%, CEEMD-GEP = 80% and CEEMD-M5 = 54%. In the second and third data set, the performance of EEMD-M5 decreases again. According to the results, hybrid GEP shows the best performance for groundwater water simulation, while M5 combined with EEMD is not recommended for the simulation due to its weak performance.
| Type de document: | Article |
|---|---|
| Mots-clés libres: | groundwater modeling; statistical modeling; signal processing; empirical mode decomposition; hybrid model |
| Centre: | Centre Eau Terre Environnement |
| Date de dépôt: | 15 oct. 2021 17:43 |
| Dernière modification: | 15 févr. 2022 20:35 |
| URI: | https://espace.inrs.ca/id/eprint/11997 |
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