Membrane　fouling　has　become　a　serious　issue　in　membrane　bioreactor　(MBR)　and　may　destroy　the　operation　of　the　wastewater　treatment　process　(WWTP).　The　goal　of　this　article　is　to　design　a　data-driven　intelligent　warning　method　for　warning　the　future　events　of　membrane　fouling　in　MBR.　The　main　novelties　of　the　proposed　method　are　threefold.　First,　a　soft-computing　model,　based　on　the　recurrent　fuzzy　neural　network　(RFNN),　was　proposed　to　identify　the　real-time　values　of　membrane　permeability.　Second,　a　multistep　prediction　strategy　was　designed　to　predict　the　multiple　outputs　of　membrane　permeability　accurately　by　decreasing　the　error　accumulation　over　the　predictive　horizon.　Third,　a　warning　detection　algorithm,　using　the　state　comprehensive　evaluation　(SCE)　method,　was　developed　to　evaluate　the　pollution　levels　of　MBR.　Finally,　the　proposed　method　was　inserted　into　a　warning　system　to　complete　the　predicting　and　warning　missions　and　further　tested　in　the　real　plants　to　evaluate　its　efficiency　and　effectiveness.　Experimental　results　have　verified　the　benefits　of　the　proposed　method.