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Influence of Pre-Processing Algorithms on Surface Water TDS Estimation Using Artificial Intelligence Models: A Case Study of the Karoon River

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Iranian Journal of Science and Technology, Transactions of Civil Engineering Aims and scope Submit manuscript

Abstract

One of the essential indicators of surface water quality is total dissolved solids (TDS) that is prominent for both short- and long-term water resources management. In this study, in order to resolve the non-stationarity challenges faced by water quality parameters (WQPs), variational mode decomposition (VMD) combined with two unsupervised artificial intelligence (AI) models, artificial neural network (ANN) and extreme learning machine (ELM), was used. Crow search algorithm (CSA) was recruited to train the ANN and ELM networks and find the optimum weight and bias values. To validate the constructed hybrid VMD-ANN/ELM-CSA models, Molasani station in Karoon River was considered to model TDS at monthly timescale. The WQPs time-series data are simultaneously factorized into respective intrinsic mode functions (IMFs) using VMD algorithm. The proposed VMD-ELM-CSA model shows the best performance at considered station, in comparison with the comparative standalone and hybrid models, with maximum values of R2 = 0.97 and Ens = 0.96 at testing period. Results show that using VMD could reduce RMSE about 37 and 12% when coupled with ANN-CSA and ELM-CSA, respectively, at Molasani station. It was concluded that the CSA optimization algorithm and VMD data decomposition technique could improve the prediction accuracy of the AI models and may be recommended for accurate WQPs prediction.

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Authors and Affiliations

  1. Department of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran

    Javad Panahi & Reza Mastouri

  2. Department of Water Engineering, kermanshah Branch, Islamic Azad University, Kermanshah, Iran

    Saeid Shabanlou

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  1. Javad Panahi
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  2. Reza Mastouri
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  3. Saeid Shabanlou
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Correspondence to Reza Mastouri.

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Panahi, J., Mastouri, R. & Shabanlou, S. Influence of Pre-Processing Algorithms on Surface Water TDS Estimation Using Artificial Intelligence Models: A Case Study of the Karoon River. Iran J Sci Technol Trans Civ Eng 47, 585–598 (2023). https://doi.org/10.1007/s40996-022-00928-4

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