ArticleEnvironmental science and pollution research international2017
Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China.
Article in Environmental science and pollution research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
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- LSTM-TCN: dissolved oxygen prediction in aquaculture, based on combined model of long short-term memory network and temporal convolutional network.Environmental science and pollution research international · 2022Article
- Dissolved Oxygen Concentration Prediction Model Based on WT-MIC-GRU-A Case Study in Dish-Shaped Lakes of Poyang Lake.Entropy (Basel, Switzerland) · 2022Article
- A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism.Environmental science and pollution research international · 2021Article
- Assessment of the total organic carbon employing the different nature-inspired approaches in the Nakdong River, South Korea.Environmental monitoring and assessment · 2021Article
- Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia.Environmental monitoring and assessment · 2021Article
- Estimation of daily dissolved oxygen concentration for river water quality using conventional regression analysis, multivariate adaptive regression splines, and TreeNet techniques.Environmental monitoring and assessment · 2020Article
- Online prediction of effluent COD in the anaerobic wastewater treatment system based on PCA-LSSVM algorithm.Environmental science and pollution research international · 2019Article
- Application of experimental design for the optimization of artificial neural network-based water quality model: a case study of dissolved oxygen prediction.Environmental science and pollution research international · 2018Article
- Estimation of sodium adsorption ratio indicator using data mining methods: a case study in Urmia Lake basin, Iran.Environmental science and pollution research international · 2018Article
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Authors and funding
4 authors.
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Abstract
Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the rural-suburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R
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