multi-output adaptive neuro-fuzzy inference system for prediction of dissolved metal levels in acid rock drainage: a case study

Pyrite oxidation, acid rock drainage (ard) generation, and associated release and transport of toxic metals are a major environmental concern for the mining industry. estimation of the metal loading in ard is a major task in developing an appropriate remediation strategy. in this work, an expert system, the multi- output adaptive neuro-fuzzy inference system (manfis), is used for estimation of metal concentrations in the shur river, resulting from ard at the sarcheshmeh porphyry copper deposit, se of iran. concentrations of cu, fe, mn, and zn are predicted using the ph, and the sulfate (so4) and magnesium (mg) concentrations in the shur river as inputs to manfis. three manfis models are implemented, grid partitioning (gp), subtractive clustering method (scm), and fuzzy c-means clustering method (fcm). a comparison is made between these three models, and the results obtained show the superiority of the manfis-scm model. these results indicate that the manfis-scm model has a potential for estimation of the metals with a high degree of accuracy and robustness.