Since　weather　has　a　huge　impact　on　the　wastewater　treatment　process　(WWTP),　the　prediction　accuracy　for　the　Biochemical　Oxygen　Demand　(BOD)　concentration　in　WWTP　would　degenerate　if　using　only　one　single　artificial　neural　network　as　the　model　for　soft　measurement　method.　Aiming　to　solve　this　problem,　the　present　study　proposes　a　novel　hybrid　scheme　using　a　modular　neural　network　(MNN)　combining　with　the　factor　of　weather　condition.　First,　discriminative　features　among　different　weather　groups　are　selected　to　ensure　a　high　accuracy　for　sample　clustering　based　on　weather　conditions.　Second,　the　samples　are　clustered　based　on　a　density-based　clustering　algorithm　using　the　discriminative　features.　Third,　the　clustered　samples　are　input　to　each　module　in　MNN,　with　the　auxiliary　variables　correlated　with　BOD　prediction　input　to　the　corresponding　model.　Finally,　a　constructive　radial　basis　function　neural　network　with　the　error-correction　algorithm　is　used　as　the　model　for　each　subnetwork　to　predict　BOD　concentration.　The　proposed　scheme　is　evaluated　on　a　standard　wastewater　treatment　platform-Benchmark　Simulation　Model　1　(BSM1).　Experimental　results　demonstrate　the　performance　improvement　of　the　proposed　scheme　on　the　prediction　accuracy　for　BOD　concentration　in　WWTP.　Besides,　the　training　time　is　shortened　and　the　network　structure　is　compact.