S. Pourmanafi

From low-cost measurements to rapid water quality monitoring: a hybrid machine learning-optimization framework for predicting laboratory-intensive parameters.

Reliable estimation of laboratory-intensive water-quality variables remains challenging in spatially heterogeneous river systems. This study developed a machine-learning framework for predicting BOD, COD, DO, NH₄, NO₂, NO₃, and TA across major rivers of Khuzestan Province, Iran, using a 12-year monitoring dataset. Afte...