Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387369365> ?p ?o ?g. }
- W4387369365 endingPage "167591" @default.
- W4387369365 startingPage "167591" @default.
- W4387369365 abstract "Accurate prediction of ammonia nitrogen concentration in water is of great significance for urban water quality management and pollution early warning. In order to improve the prediction accuracy of ammonia nitrogen concentration in water, this study developed a novel model based on graph neural networks called Feature Multi-level Attention Spatio-Temporal Graph Residual Network (FMA-STGRN). The FMA-STGRN model utilizes external influencing factors such as meteorological factors and point of interest data, as well as the spatio-temporal correlation information of ammonia nitrogen concentration between water quality monitoring stations, to accurately predict the concentration of ammonia nitrogen in water. The model consists of four main components: feature multi-level attention module, spatial graph convolution module, temporal-domain residual decomposition module, and feature fusion and output module. Through the organic combination of these four modules, FMA-STGRN can more effectively explore the complex spatio-temporal correlation relationships between water quality monitoring stations and more accurately integrate and utilize external influencing factors, thereby improving the prediction accuracy of ammonia nitrogen concentration in water. Experimental results show that the FMA-STGRN model outperforms other benchmark models such as RF, MART, MLP, LSTM, GRU, ST-GCN, and ST-GAT in various aspects. In addition, a series of feature ablation experiments were conducted to further reveal the key contributions of meteorological factors and point of interest data to the model performance. Overall, our research provides a powerful and practical tool for water quality monitoring and urban water management, with broad application prospects." @default.
- W4387369365 created "2023-10-06" @default.
- W4387369365 creator A5014390894 @default.
- W4387369365 creator A5015978208 @default.
- W4387369365 creator A5029239459 @default.
- W4387369365 creator A5036486114 @default.
- W4387369365 creator A5040026305 @default.
- W4387369365 creator A5047205302 @default.
- W4387369365 creator A5079991786 @default.
- W4387369365 date "2024-01-01" @default.
- W4387369365 modified "2023-10-13" @default.
- W4387369365 title "Feature multi-level attention spatio-temporal graph residual network: A novel approach to ammonia nitrogen concentration prediction in water bodies by integrating external influences and spatio-temporal correlations" @default.
- W4387369365 cites W1973693963 @default.
- W4387369365 cites W2038112969 @default.
- W4387369365 cites W2124178519 @default.
- W4387369365 cites W2125223451 @default.
- W4387369365 cites W2321707316 @default.
- W4387369365 cites W2610034660 @default.
- W4387369365 cites W2808282876 @default.
- W4387369365 cites W2913323966 @default.
- W4387369365 cites W2914868446 @default.
- W4387369365 cites W2969685114 @default.
- W4387369365 cites W2976945603 @default.
- W4387369365 cites W3005619874 @default.
- W4387369365 cites W3008408706 @default.
- W4387369365 cites W3016208458 @default.
- W4387369365 cites W3021216301 @default.
- W4387369365 cites W3040828009 @default.
- W4387369365 cites W3042384762 @default.
- W4387369365 cites W3046781693 @default.
- W4387369365 cites W3084107721 @default.
- W4387369365 cites W3121087702 @default.
- W4387369365 cites W3123909522 @default.
- W4387369365 cites W3135674174 @default.
- W4387369365 cites W3145821786 @default.
- W4387369365 cites W3174830053 @default.
- W4387369365 cites W3189604654 @default.
- W4387369365 cites W3190011592 @default.
- W4387369365 cites W3205710682 @default.
- W4387369365 cites W4212871224 @default.
- W4387369365 cites W4223478633 @default.
- W4387369365 cites W4280494158 @default.
- W4387369365 cites W4285138331 @default.
- W4387369365 cites W4285489795 @default.
- W4387369365 cites W4297382430 @default.
- W4387369365 cites W4376620236 @default.
- W4387369365 doi "https://doi.org/10.1016/j.scitotenv.2023.167591" @default.
- W4387369365 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37802332" @default.
- W4387369365 hasPublicationYear "2024" @default.
- W4387369365 type Work @default.
- W4387369365 citedByCount "0" @default.
- W4387369365 crossrefType "journal-article" @default.
- W4387369365 hasAuthorship W4387369365A5014390894 @default.
- W4387369365 hasAuthorship W4387369365A5015978208 @default.
- W4387369365 hasAuthorship W4387369365A5029239459 @default.
- W4387369365 hasAuthorship W4387369365A5036486114 @default.
- W4387369365 hasAuthorship W4387369365A5040026305 @default.
- W4387369365 hasAuthorship W4387369365A5047205302 @default.
- W4387369365 hasAuthorship W4387369365A5079991786 @default.
- W4387369365 hasBestOaLocation W43873693651 @default.
- W4387369365 hasConcept C11413529 @default.
- W4387369365 hasConcept C124101348 @default.
- W4387369365 hasConcept C132525143 @default.
- W4387369365 hasConcept C138885662 @default.
- W4387369365 hasConcept C154945302 @default.
- W4387369365 hasConcept C155512373 @default.
- W4387369365 hasConcept C18903297 @default.
- W4387369365 hasConcept C2776401178 @default.
- W4387369365 hasConcept C2780797713 @default.
- W4387369365 hasConcept C39432304 @default.
- W4387369365 hasConcept C41008148 @default.
- W4387369365 hasConcept C41895202 @default.
- W4387369365 hasConcept C80444323 @default.
- W4387369365 hasConcept C86803240 @default.
- W4387369365 hasConceptScore W4387369365C11413529 @default.
- W4387369365 hasConceptScore W4387369365C124101348 @default.
- W4387369365 hasConceptScore W4387369365C132525143 @default.
- W4387369365 hasConceptScore W4387369365C138885662 @default.
- W4387369365 hasConceptScore W4387369365C154945302 @default.
- W4387369365 hasConceptScore W4387369365C155512373 @default.
- W4387369365 hasConceptScore W4387369365C18903297 @default.
- W4387369365 hasConceptScore W4387369365C2776401178 @default.
- W4387369365 hasConceptScore W4387369365C2780797713 @default.
- W4387369365 hasConceptScore W4387369365C39432304 @default.
- W4387369365 hasConceptScore W4387369365C41008148 @default.
- W4387369365 hasConceptScore W4387369365C41895202 @default.
- W4387369365 hasConceptScore W4387369365C80444323 @default.
- W4387369365 hasConceptScore W4387369365C86803240 @default.
- W4387369365 hasLocation W43873693651 @default.
- W4387369365 hasLocation W43873693652 @default.
- W4387369365 hasOpenAccess W4387369365 @default.
- W4387369365 hasPrimaryLocation W43873693651 @default.
- W4387369365 hasRelatedWork W2498789492 @default.
- W4387369365 hasRelatedWork W2521347458 @default.
- W4387369365 hasRelatedWork W2560215812 @default.
- W4387369365 hasRelatedWork W2788972299 @default.
- W4387369365 hasRelatedWork W2925692864 @default.
- W4387369365 hasRelatedWork W2972212393 @default.