Estimation of total dissolved solids, electrical conductivity, salinity and groundwater levels using novel learning mach
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ORIGINAL ARTICLE
Estimation of total dissolved solids, electrical conductivity, salinity and groundwater levels using novel learning machines Mojtaba Poursaeid1 · Reza Mastouri1 · Saeid Shabanlou2 · Mohsen Najarchi1 Received: 20 April 2019 / Accepted: 11 September 2020 / Published online: 25 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In this study, the groundwater parameters including electrical conductivity (EC), salinity, total dissolved solids (TDS) and groundwater level (GWL) for a 15-year time series (from 2002 to 2017) of the Mighan Plain, located in Markazi Province, Iran, were simulated using a hybrid meta heuristic artificial intelligence (AI) model called “wavelet self-adaptive extreme learning machine” (WSAELM) for the first time. Initially, the detection of the most significant lags of the time-series data was conducted using the autocorrelation function (ACF) and the partial autocorrelation function (PACF) analyses. By using these lags, four WSAELM models were defined and then the superior models in simulating the TDS, EC, salinity and GWL were introduced. The values of the determination coefficient (R2), Variance Accounted for (VAF) and the Nash–Sutcliffe efficiency coefficient (NSC) for the superior model simulating salinity were computed to be 0.991, 98.124 and 0.980, respectively. Also, approximately 44% of the TDS values modeled by the best model had an error less than 10%, while roughly a third of the TDS values estimated by the model had an error more than 15%. The findings indicated that the proposed hybrid model underestimated the GWL parameter, while it performed in an overestimate way for other parameters. The results of the uncertainty analysis showed the low width of uncertainty for GWL (WUB = ± 0.798), TDS (WUB = ± 5.035), EC (WUB = ± 6.425) and salinity (WUB = ± 66.650). Keywords Electrical conductivity · Groundwater level · Salinity · Self-adaptive extreme learning machine · Total dissolved solids · Wavelet transform
Introduction Due to climate change and global warming, different parts of Earth, especially arid and semi-arid regions have been faced by serious challenges for supplying fresh water for various purposes such as urban, industrial and agricultural consumptions. Iran, located in the Southeast Asia region has a semi-arid climate, thus has experienced many problems for supplying drinking water demands in recent years. In this country, the major part of fresh water is supplied from water tables. In recent years, due to excessive withdrawals of water from different aquifers, hazardous problems such as the groundwater scarcity, the formation of sinkholes, * Reza Mastouri r‑mastouri@iau‑arak.ac.ir 1
Department of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran
Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
2
salinization of water tables, drying of lakes have raised (Barzegar and Moghaddam 2016; Malekzadeh et al. 2019a). Therefore, investigations of qualitative
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