Abstract
Predicting Water Quality Parameters in a Complex River System
Highlights
Rivers have been the most utilized natural water source due to their availability and accessibility; this has prompted the growth of civilization and industries close to river banks (Mustafa et al, 2017)
As for scheme 1, we found that the R2 score of train data for all water quality parameters is more than 0.80, which signifies a satisfactory result in predicting the train data
The values of six water quality parameters, i.e. dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), pH, ammonia nitrogen (NH3-N) and suspended solids (SS) of station 1 were predicted by using the Support Vector Machine (SVM) model
Summary
Rivers have been the most utilized natural water source due to their availability and accessibility; this has prompted the growth of civilization and industries close to river banks (Mustafa et al, 2017). Water quality monitoring and prediction allows a manager to identify a suitable option that satisfies a wide range of conditions. The water parameters such as turbidity, electrical conductivity and dissolved solids in water, for example, describe a complex process controlled by ecological, hydrological and hydrodynamic factors that operate at a wide range of spatiotemporal scales (Najah et al, 2009). The water quality index (WQI) analysis of rivers is a popular topic in physical
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