Nowadays,　freshwater　resources　are　facing　numerous　crises　and　pressures,　resulting　from　both　artificial　and　natural　process,　so　it　is　crucial　to　predict　the　water　quality　for　the　department　of　water　environment　protection.　This　paper　proposes　a　hybrid　optimized　algorithm　involving　a　particle　swarm　optimization　(PSO)　and　genetic　algorithm　(GA)　combined　BP　neural　network　that　can　predict　the　water　quality　in　time　series　and　has　good　performance　in　Beihai　Lake　in　Beijing.　The　data　sets　consist　of　six　water　quality　parameters　which　include　Hydrogen　Ion　Concentration　(pH),　Chlorophyll-a　(CHLA),　Hydrogenated　Amine　(NH4H),　Dissolved　Oxygen　(DO),　Biochemical　Oxygen　Demand　(BOD),　and　electrical　conductivity　(EC).　The　performance　of　the　model　was　assessed　through　the　absolute　percentage　error　the　mean　absolute　percentage　error　(MAPE),　the　root　mean　square　error　(RMSE),　and　the　coefficient　of　determination.　Study　results　show　that　the　model　based　on　PSO　and　GA　to　optimize　the　BP　neural　network　is　able　to　predict　the　water　quality　parameters　with　reasonable　accuracy,　suggesting　that　the　model　is　a　valuable　tool　for　lake　water　quality　estimation.　The　results　show　that　the　hybrid　optimized　BP　model　has　a　higher　prediction　capacity　and　better　robustness　of　water　quality　parameters　compared　with　the　traditional　BP　neural　network,　the　PSO-optimized　BP　neural　network,　and　the　GA-optimized　BP　neural　network.