Abstract
Due to its heterogeneous and complex nature, groundwater modeling needs great effort to quantify the aquifer, a crucial tool for policymakers and hydrogeologists to understand the variations in groundwater levels (GWL). This study proposed a set of supervised machine learning (ML) models to delineate the GWL changes in the Zarand-Saveh complex aquifer in Iran using 15-year (2005–2020) monthly dataset. The wavelet transform (WT) procedure was also used to improve the GWL prediction ability of ML models for 3-month horizons using input datasets of precipitation, evapotranspiration, temperature, and GWL. The four well-accepted standalone ML methods, i.e., artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), and least square support vector machine (LSSVM), were implemented and compared with the hybrid wavelet conjunction models. The methods were compared based on root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Nash–Sutcliffe efficiency (NSE). Comparison outcomes showed that the hybrid wavelet–ML considerably improved the standalone model results. The wavelet transform-least square support vector machine (WT-LSSVM) model was superior to other standalone and hybrid wavelet–ML methods to predict GWL. The best GWL predictions were acquired from the WT-LSSVM model with input scenario 5 involving all influential variables, and this model produced RMSE, MAE, R, and NSE as 0.05, 0.04, 0.99, and 0.99 for 1 month ahead of GWL prediction, while the corresponding values were obtained as 0.18, 0.14, 0.95, and 0.90 for 3 months ahead of GWL prediction, respectively.
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Abbreviations
- AI:
-
Artificial intelligence
- ANFIS :
-
Adaptive neuro-fuzzy inference system
- ANN :
-
Artificial neural network
- CEEMD :
-
Complementary ensemble empirical mode decomposition
- EEMD :
-
Ensemble empirical mode decomposition
- EMD :
-
Empirical mode decomposition
- FCM :
-
Fuzzy C-means clustering
- FT :
-
Fourier transform
- GEP:
-
Gene expression programming
- GMDH :
-
Group method of data handling
- GP :
-
Genetic programming
- GWL :
-
Groundwater level
- LM :
-
Levenberg–Marquardt
- LSSVM :
-
Least square support vector machine
- MAE :
-
Mean absolute error
- MF :
-
Membership functions
- ML :
-
Machine learning
- MLP :
-
Multi-layer perceptron
- MLR :
-
Multi-layer regression
- NSE :
-
Nash–Sutcliffe efficiency
- R :
-
Correlation coefficient
- RMSE :
-
Root mean square error
- SVR :
-
Support vector regression
- WA :
-
Whale algorithm
- WT :
-
Wavelet transform
- WT-ANFIS :
-
Wavelet transform-adaptive neuro-fuzzy inference system
- WT-ANN :
-
Wavelet transform-artificial neural network
- WT-GMDH :
-
Wavelet transform-group method of data handling
- WT-LSSVM :
-
Wavelet transform-least square support vector machine
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S. Samani and M. Vadiati analyzed and interpreted data and contributed to writing the manuscript. Z. Nejatijahromi and b. Etebari collected data and had contributed to drafting manuscript preparation. O. Kisi was involved in revising the manuscript critically for important intellectual content. All authors read and approved the final manuscript.
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Highlights
• Performance of machine learning models, ANN, ANFIS, GMDH, and LSSVM, was investigated in GWL prediction.
• ML models were compared with wavelet conjunction models.
• Wavelet transform noticeably enhances standalone ML models’ accuracy.
• ML’s performance was improved by using WT for 2 and 3 months ahead of GWL prediction.
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Samani, S., Vadiati, M., Nejatijahromi, Z. et al. Groundwater level response identification by hybrid wavelet–machine learning conjunction models using meteorological data. Environ Sci Pollut Res 30, 22863–22884 (2023). https://doi.org/10.1007/s11356-022-23686-2
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DOI: https://doi.org/10.1007/s11356-022-23686-2