Water　quality　data　cleaning　is　important　for　the　management　of　water　environments.　A　framework　for　water　quality　time　series　cleaning　is　proposed　in　this　paper.　Considering　the　nonlinear　relationships　among　water　quality　indicators,　support　vector　regression　(SVR)　is　used　to　forecast　water　quality　indicators　when　some　indicators　are　missing　or　when　they　show　abnormal　values　at　a　certain　point　in　time.　Considering　the　time　series　of　water　quality　information,　long　short-term　memory　(LSTM)　networks　are　used　to　forecast　water　quality　indicators　when　all　indicators　are　missing　at　a　certain　point　in　time.　A　parallel　model　based　on　particle　swarm　optimization　(PSO)　and　LSTM　is　realized　based　on　a　microservices　architecture　to　improve　the　efficiency　of　model　execution　and　the　predictive　accuracy　of　the　LSTM　networks.　The　performance　of　the　model　is　evaluated　in　terms　of　the　mean　absolute　error　(MAE)　and　root-mean-square　error　(RMSE).　Inlet　water　quality　data　from　a　wastewater　treatment　plant　in　Gaobeidian,　Beijing,　China　is　considered　as　a　case　study　to　examine　the　effectiveness　of　this　approach.　The　experimental　results　reveal　that　this　model　has　better　predictive　accuracy　than　other　data-driven　models　because　of　smaller　MAE　and　RMSE　and　has　an　advantage　in　terms　of　time　consumption　compared　with　standalone　serial　algorithms.