Predicting　the　level　of　dissolved　oxygen　(DO)　is　an　important　issue　ensuring　the　sustainability　of　the　inhabitants　of　a　river.　A　prediction　model　can　predict　the　DO　level　using　a　historical　dataset　with　regard　to　water　temperature,　pH,　and　specific　conductance　for　a　given　river.　The　model　can　be　built　using　sophisticated　computational　procedures　such　as　multi-layer　perceptron-based　artificial　neural　networks.　Different　types　of　networks　can　be　constructed　for　this　purpose.　In　this　study,　the　authors　constructed　three　networks,　namely,　multi-verse　optimizer　(MVO),　black　hole　algorithm　(BHA),　and　shuffled　complex　evolution　(SCE).　The　networks　were　trained　using　the　datasets　collected　from　the　Klamath　River　Station,　Oregon,　USA,　for　the　period　2015-2018.　We　found　that　the　trained　networks　could　predict　the　DO　level　of　2019.　We　also　found　that　both　BHA-　and　SCE-based　networks　could　predict　the　level　of　DO　using　a　relatively　simple　configuration　compared　to　that　of　MVO.　From　the　viewpoints　of　absolute　errors　and　Pearson＇s　correlation　coefficient,　MVO-　and　SCE-based　networks　performed　better　than　BHA-based　networks.　In　synopsis,　the　authors　recommend　MVO-　and　MLP-based　artificial　neural　networks　for　predicting　the　DO　level　of　a　river.