Nowadays,　the　deterioration　of　water　resources　leads　to　negative　ecological　impacts.　To　effectively　inhibit　the　deterioration　of　water　resources,　a　water　quality　prediction　model　based　on　enhanced　transformer,　sliding　block,　and　channel　independence　(WaterTS)　is　proposed　by　comprehensively　analyzing　the　indicators　of　water　resources　and　making　long-term　predictions　of　the　dissolved　oxygen　index.　WaterTS　adopts　a　sliding　block　method　to　extract　the　short-term　temporal　features　of　the　water　quality　series　and　combine　them　with　channel　independence　to　make　independent　predictions　of　multi-featured　data.　Moreover,　it　upgrades　the　internal　encoder　structure　of　the　transformer　and　improves　the　attention　mechanism　to　Probsparse-attention　and　Auto-Correlation　to　speed　up　the　prediction　speed.　Furthermore,　Post　LayerNormal　is　adjusted　to　Pre　LayerNormal,　which　makes　the　training　gradient　more　stable　and　enhances　the　accuracy　of　predictions.　Experiments　are　conducted　using　real-world　water　environment　data,　and　comparison　results　with　state-of-the-art　prediction　models　show　that　the　WaterTS　achieves　accurate　predictions　on　both　short-term　and　long-term　water　quality　data.　©　2024　IEEE.