Evaluation and prediction of Seybouse river water quality using response surface methodology and machine learning.

Climate change and human activity are posing an increasing danger to water quality. Surface water in Algeria, especially in semi-arid areas, is under increasing pressure from industrial and urban discharges, and there is still a dearth of scientific information regarding its quality and appropriateness for irrigation. This study's goal was to assess the physico chemical properties and surface water irrigation quality at nine locations along the Seybouse River in northeastern Algeria, thereby addressing an important lack of information in an area with little monitoring. The current study looked at nine sampling sites to assess the river Water Quality Index (WQI), sodium absorption rate (SAR), soluble sodium percentage (SSP), and Kelly Ratio (KR). Artificial Neural Network (ANN) and Response Surface Methodology (RSM) inspired machine learning (ML) approaches were used to enhance the evaluations utilizing Principal Component Analysis (PCA) data, representing 82.08% of the entire collection of data. The study's findings demonstrate that the ANN forecasting of WQI, SSP, SAR, and KR is more accurate than the RSM models. The ANN models achieved superior predictive performance with R