Simulation of Water Level Fluctuations, Chlorine, Bicarbonate in groundwater using a Hybrid Learning Machine

Document Type : Research paper

Authors

1 Ph.D. Candidate, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

2 Assistance Professor, Depatment of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

3 Associated Professor, Depatment of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

Abstract

Modeling qualitative and quantitative parameters of groundwater as one of the main water supply resources is crucially important. Due to recent climate changes in Iran, precipitation patterns have been dramatically altered leading to excess withdrawal. In this paper, two meta-heuristic artificial intelligence models are presented to simulate monthly time-series data of quantitative (groundwater level) and qualitative (chlorine and bicarbonate) parameters of groundwater within an observational well situated in the city of Kermanshah, Iran from 2005 to 2018. To define the hybrid artificial intelligence model, the extreme learning machine (ELM), differential evolutionary (DE) algorithms are combined with the wavelet transform and the Self-adaptive extreme learning machine (SAELM) and wavelet self-adaptive extreme learning machine (WSAELM) models are developed. It is worth mentioning that the autocorrelation function is utilized for detecting effective lags of the time-series data. Moreover, 70% of the observed data are used for training the artificial intelligence models and the remaining 30% for testing them. Then, using these influencing lags, different models are defined for the SAELM and WSAELM models. Also, different mother wavelets are assessed to choose the most optimal one for decomposing signals of the time-series data. After that, superior models for simulating GWL, Cl and HCO3 are introduced by performing a sensitivity analysis. For instance, the values of the correlation coefficient (R), the variance accounted for (VAF) and Nash-Sutcliff efficiency Coefficient (NSC) for the superior WSAELM model are obtained to be 0.988, 97.450 and 0.973, respectively. It should be noted that for forecasting HCO3 the lags (t-1), (t-2), (t-3) and (t-4) are identified as the most influencing lags of the time-series data. The utilization of an uncertainty analysis exhibited that the superior model has an underestimated performance in simulating Cl and HCO3. Finally, three formulae are presented for computing GWL, Cl and HCO3.

Keywords

References

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Hydrogeology
Volume 6, Issue 1
Publisher
September 2021
Pages 99-113

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