Matches in SemOpenAlex for { <https://semopenalex.org/work/W2050998616> ?p ?o ?g. }
Showing items 1 to 95 of
95
with 100 items per page.
- W2050998616 endingPage "16" @default.
- W2050998616 startingPage "8" @default.
- W2050998616 abstract "China's fast pace industrialization and growing population has led to several accidental surface water pollution events in the last decades. The government of China, after the 2005 Songhua River incident, has pushed for the development of early warning systems (EWS) for drinking water source protection. However, there are still many weaknesses in EWS in China such as the lack of pollution monitoring and advanced water quality prediction models. The application of Data Driven Models (DDM) such as Artificial Neural Networks (ANN) has acquired recent attention as an alternative to physical models. For a case study in a south industrial city in China, a DDM based on genetic algorithm (GA) and ANN was tested to increase the response time of the city's EWS. The GA-ANN model was used to predict NH3–N, CODmn and TOC variables at station B 2 h ahead of time while showing the most sensitive input variables available at station A, 12 km upstream. For NH3–N, the most sensitive input variables were TOC, CODmn, TP, NH3–N and Turbidity with model performance giving a mean square error (MSE) of 0.0033, mean percent error (MPE) of 6% and regression (R) of 92%. For COD, the most sensitive input variables were Turbidity and CODmn with model performance giving a MSE of 0.201, MPE of 5% and R of 0.87. For TOC, the most sensitive input variables were Turbidity and CODmn with model performance giving a MSE of 0.101, MPE of 2% and R of 0.94. In addition, the GA-ANN model performed better for 8 h ahead of time. For future studies, the use of a GA-ANN modelling technique can be very useful for water quality prediction in Chinese monitoring stations which already measure and have immediately available water quality data." @default.
- W2050998616 created "2016-06-24" @default.
- W2050998616 creator A5013763730 @default.
- W2050998616 creator A5031930852 @default.
- W2050998616 creator A5085896976 @default.
- W2050998616 creator A5087095666 @default.
- W2050998616 creator A5091007059 @default.
- W2050998616 date "2014-10-01" @default.
- W2050998616 modified "2023-10-15" @default.
- W2050998616 title "A hybrid evolutionary data driven model for river water quality early warning" @default.
- W2050998616 cites W1573426874 @default.
- W2050998616 cites W2004630602 @default.
- W2050998616 cites W2014626999 @default.
- W2050998616 cites W2064820716 @default.
- W2050998616 cites W2071707222 @default.
- W2050998616 cites W2092474617 @default.
- W2050998616 cites W2095239580 @default.
- W2050998616 cites W2109563136 @default.
- W2050998616 cites W2142702279 @default.
- W2050998616 doi "https://doi.org/10.1016/j.jenvman.2014.04.017" @default.
- W2050998616 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24833523" @default.
- W2050998616 hasPublicationYear "2014" @default.
- W2050998616 type Work @default.
- W2050998616 sameAs 2050998616 @default.
- W2050998616 citedByCount "40" @default.
- W2050998616 countsByYear W20509986162014 @default.
- W2050998616 countsByYear W20509986162015 @default.
- W2050998616 countsByYear W20509986162016 @default.
- W2050998616 countsByYear W20509986162017 @default.
- W2050998616 countsByYear W20509986162019 @default.
- W2050998616 countsByYear W20509986162020 @default.
- W2050998616 countsByYear W20509986162021 @default.
- W2050998616 countsByYear W20509986162022 @default.
- W2050998616 countsByYear W20509986162023 @default.
- W2050998616 crossrefType "journal-article" @default.
- W2050998616 hasAuthorship W2050998616A5013763730 @default.
- W2050998616 hasAuthorship W2050998616A5031930852 @default.
- W2050998616 hasAuthorship W2050998616A5085896976 @default.
- W2050998616 hasAuthorship W2050998616A5087095666 @default.
- W2050998616 hasAuthorship W2050998616A5091007059 @default.
- W2050998616 hasConcept C105795698 @default.
- W2050998616 hasConcept C127413603 @default.
- W2050998616 hasConcept C139945424 @default.
- W2050998616 hasConcept C146978453 @default.
- W2050998616 hasConcept C187320778 @default.
- W2050998616 hasConcept C18903297 @default.
- W2050998616 hasConcept C191172861 @default.
- W2050998616 hasConcept C2780797713 @default.
- W2050998616 hasConcept C29825287 @default.
- W2050998616 hasConcept C33923547 @default.
- W2050998616 hasConcept C39432304 @default.
- W2050998616 hasConcept C521259446 @default.
- W2050998616 hasConcept C64016661 @default.
- W2050998616 hasConcept C76155785 @default.
- W2050998616 hasConcept C76886044 @default.
- W2050998616 hasConcept C86803240 @default.
- W2050998616 hasConcept C87717796 @default.
- W2050998616 hasConceptScore W2050998616C105795698 @default.
- W2050998616 hasConceptScore W2050998616C127413603 @default.
- W2050998616 hasConceptScore W2050998616C139945424 @default.
- W2050998616 hasConceptScore W2050998616C146978453 @default.
- W2050998616 hasConceptScore W2050998616C187320778 @default.
- W2050998616 hasConceptScore W2050998616C18903297 @default.
- W2050998616 hasConceptScore W2050998616C191172861 @default.
- W2050998616 hasConceptScore W2050998616C2780797713 @default.
- W2050998616 hasConceptScore W2050998616C29825287 @default.
- W2050998616 hasConceptScore W2050998616C33923547 @default.
- W2050998616 hasConceptScore W2050998616C39432304 @default.
- W2050998616 hasConceptScore W2050998616C521259446 @default.
- W2050998616 hasConceptScore W2050998616C64016661 @default.
- W2050998616 hasConceptScore W2050998616C76155785 @default.
- W2050998616 hasConceptScore W2050998616C76886044 @default.
- W2050998616 hasConceptScore W2050998616C86803240 @default.
- W2050998616 hasConceptScore W2050998616C87717796 @default.
- W2050998616 hasLocation W20509986161 @default.
- W2050998616 hasLocation W20509986162 @default.
- W2050998616 hasOpenAccess W2050998616 @default.
- W2050998616 hasPrimaryLocation W20509986161 @default.
- W2050998616 hasRelatedWork W2356755667 @default.
- W2050998616 hasRelatedWork W2386925039 @default.
- W2050998616 hasRelatedWork W2607474063 @default.
- W2050998616 hasRelatedWork W2936713214 @default.
- W2050998616 hasRelatedWork W3018395630 @default.
- W2050998616 hasRelatedWork W3208236762 @default.
- W2050998616 hasRelatedWork W4239095282 @default.
- W2050998616 hasRelatedWork W4304690685 @default.
- W2050998616 hasRelatedWork W4360922838 @default.
- W2050998616 hasRelatedWork W2488034869 @default.
- W2050998616 hasVolume "143" @default.
- W2050998616 isParatext "false" @default.
- W2050998616 isRetracted "false" @default.
- W2050998616 magId "2050998616" @default.
- W2050998616 workType "article" @default.