Water　pollution　is　continuously　increasing　in　water　ecosystems　across　all　continents.　Surface　water　sensors　can　record　data　on　water　quality　indicators　at　regular　intervals,　and　the　associated　water　quality　sequences　show　abnormal　trends　when　extreme　weather　or　unusual　industrial　discharges　occur.　Therefore,　governments　can　take　timely　actions　to　minimize　damage　and　protect　the　water　environment　by　detecting　these　abnormal　trends　promptly.　However,　current　methods　make　it　difficult　to　interpret　different　correlations　among　water　quality　parameters　effectively.　To　solve　this　problem,　this　work　proposes　a　parameter　correlation-aware　anomaly　detection　model,　which　integrates　Dual　sliding　windows,　Convolutional　LSTM,　and　a　Deep　neural　network　with　dropout,　called　for　DCLD　short.　First,　DCLD　designs　dual　sliding　windows　to　capture　local　and　global　patterns　within　the　sequence　of　water　quality.　Second,　DCLD　adopts　a　stacked　long　short-term　memory　with　a　convolutional　neural　network　to　capture　complex　features　and　long-term　dependencies　in　the　time　series.　Third,　DCLD　uses　a　deep　neural　network　incorporating　the　dropout　algorithm　to　extract　abstract　features.　DCLD　mitigates　overfitting　risks　and　enhances　the　model＇s　generalization　capacity.　Finally,　DCLD　is　evaluated　with　two　real-world　water　quality　datasets,　and　its　anomaly　detection　accuracy　is　improved　by　5.41%　and　0.79%　on　average　over　its　peers.　©　2024　IEEE.