A Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods Improves the Prediction of Groundwater Level

Zahra Kayhomayoon; Faezeh Babaeian; Sami Ghordoyee Milan; Naser Arya Azar; Ronny Berndtsson

doi:10.3390/w14050751

journal article Open Access Feb 26, 2022

A Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods Improves the Prediction of Groundwater Level

Zahra Kayhomayoon Faezeh Babaeian Sami Ghordoyee Milan

Naser Arya Azar Ronny Berndtsson

Water Vol. 14 No. 5 pp. 751 · MDPI AG

View at Publisher Save 10.3390/w14050751

Abstract

Groundwater is a crucial source of water supply in drought conditions, and an auxiliary water source in wet seasons. Due to its increasing importance in view of climate change, predicting groundwater level (GWL) needs to be improved to enhance management. We used adaptive neuro-fuzzy inference systems (ANFIS) to predict the GWL of the Urmia aquifer in northwestern Iran under various input scenarios using precipitation, temperature, groundwater withdrawal, GWL during the previous month, and river flow. In total, 11 input patterns from various combinations of variables were developed. About 70% of the data were used to train the models, while the rest were used for validation. In a second step, several metaheuristic algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR), and differential evolution (DE) were used to improve the model and, consequently, prediction performance. The results showed that (i) RMSE, MAPE, and NSE of 0.51 m, 0.00037 m, and 0.86, respectively, were obtained for the ANFIS model using all input variables, indicating a rather poor performance, (ii) metaheuristic algorithms were able to optimize the parameters of the ANFIS model in predicting GWL, (iii) the input pattern that included all input variables resulted in the most appropriate performance with RMSE, MAPE, and NSE of 0.28 m, 0.00019 m, and 0.97, respectively, using the ANIFS-ACOR hybrid model, (iv) results of Taylor’s diagram (CC = 0.98, STD = 0.2, and RMSD = 0.30), as well as the scatterplot (R2 = 0.97), showed that best prediction was achieved by ANFIS-ACOR, and (v) temperature and evaporation exerted stronger influence on GWL prediction than groundwater withdrawal and precipitation. The findings of this study reveal that metaheuristic algorithms can significantly improve the performance of the ANFIS model in predicting GWL.

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Citations

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References

Details

Published: Feb 26, 2022
Vol/Issue: 14(5)
Pages: 751
License: View

Authors

Z

Zahra Kayhomayoon

Department of Geology, Payame Noor University, Tehran 193954697, Iran

F

Faezeh Babaeian

Department of Water Science and Engineering, Science and Research Branch, Islamic Azad University Tehran, Tehran 1477893855, Iran

S

Sami Ghordoyee Milan

Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Tehran 3391653755, Iran

N

Naser Arya Azar

Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 5166616471, Iran

R

Ronny Berndtsson

Division of Water Resources Engineering and Centre for Advanced Middle Eastern Studies, Lund University, SE-221 00 Lund, Sweden

Funding

the Centre for Advanced Middle Eastern Studies, Lund University Award: 107442

Cite This Article

Zahra Kayhomayoon, Faezeh Babaeian, Sami Ghordoyee Milan, et al. (2022). A Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods Improves the Prediction of Groundwater Level. Water, 14(5), 751. https://doi.org/10.3390/w14050751

A Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods Improves the Prediction of Groundwater Level

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