Abstract
Rivers are an important aspect of the water supply, so they play a vital role in increasing the life expectancy of living organisms. Water Quality Indicators (WQIs) can be used to determine the basic properties of water pollutants. Therefore, the high demand for accurate forecasting of water quality indicators is of great importance for understanding pollutant trends in natural currents. Field Studies performed on various rivers have shown that there is no general association between yielding water quality parameters with a permissible degree of accuracy. Over the past several decades, many models of artificial intelligence (AI) have been used to forecasting more accurate estimation of WQIs than traditional models. The current research used the multivariate adaptive regression spline (MARS) algorithm with Bat algorithm to predict five-day biochemical oxygen demand (BOD5) and chemical oxygen demand (COD) indices. To strengthen the solution suggested. Done determination of nine independent input parameters, namely electrical conductivity (EC), sodium (Na), calcium (Ca), magnesium (Mg), orthophosphate (PO), nitrite (NO2), nitrate nitrogen (NO3), turbidity, and pH.
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Al-Barmani, Z., Al-Janabi, S. (2021). Intelligent Data Mining Techniques to Verification of Water Quality Index. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_58
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