This　article　focuses　on　proposing　a　new　framework　for　self-organizing　multichannel　deep　learning　system　(SMDLS)　to　solve　the　problem　of　river　turbidity　monitoring,　which　is　one　of　the　most　critical　indicators　of　water　contamination.　First,　the　basic　architecture　of　our　proposed　SMDLS　is　established　based　on　newly　designed　fractal　modules,　each　of　which　is　composed　of　one　fixed　block　and　one　nested　block.　The　nested　block　in　the　higher　level　fractal　module　is　essentially　the　lower　level　fractal　module.　Second,　we　develop　a　novel　riverway-like　generalized　regression　loss　function　to　improve　the　robustness　of　the　proposed　SMDLS　to　distorted　labels,　which　is　caused　by　small　noise　(e.g.,　the　temperature　drift　noise　and　wet　drift　noise　of　turbidity　sensors)　in　most　cases　and　big　noise　(e.g.,　the　outlier　noise　due　to　solar　radiation　shielded　by　mountains)　on　rare　occasions.　Finally,　we　further　introduce　an　ensemble-induced　multichannel　(EMC)　fusion　to　modify　the　proposed　SMDLS　for　strengthening　its　performance　and　generalization　ability.　Experiment　conducted　on　large-size　river　turbidity　monitoring　database　demonstrates　that　our　system　considerably　outperforms　the　existing　state-of-the-art　learning　systems.