Water Environmental Functional Zoning at County Level and Environmental Contamination Carrying Capacity Accounting in the Mainstream of Xiaofu River

Ding, Shaoxuan; Wang, Fangshun; Sun, Xining; Ding, Jincheng; Lü, Jie

doi:10.3390/w14040615

Cited by 4 publications

(1 citation statement)

References 13 publications

(10 reference statements)

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“…The Xiaofu River basin is located at 36 • 25 N~37 • 07 N, 117 • 42 E~118 • 08 E. The watershed is 40 km wide from east to west and 76 km long from north to south, and the watershed area is 1705 km 2 . The main tributaries are the Fanyang River, Banyang River, Mansi River, Gan River, Zhulong West River and others [36]. Since the 1980s, the Xiaofu River has been used as a sewage channel for factories, mines, enterprises, and residents along the river.…”

Section: Study Areamentioning

confidence: 99%

Ensemble Empirical Mode Decomposition and a Long Short-Term Memory Neural Network for Surface Water Quality Prediction of the Xiaofu River, China

Luo

Zhang

Dong

et al. 2023

Water

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Water quality prediction is an important part of water pollution prevention and control. Using a long short-term memory (LSTM) neural network to predict water quality can solve the problem that comprehensive water quality models are too complex and difficult to apply. However, as water quality time series are generally multiperiod hybrid time series, which have strongly nonlinear and nonstationary characteristics, the prediction accuracy of LSTM for water quality is not high. The ensemble empirical mode decomposition (EEMD) method can decompose the multiperiod hybrid water quality time series into several simpler single-period components. To improve the accuracy of surface water quality prediction, a water quality prediction model based on EEMD–LSTM was developed in this paper. The water quality time series was first decomposed into several intrinsic mode function components and one residual item, and then these components were used as the input of LSTM to predict water quality. The model was trained and validated using four water quality parameters (NH3-N, pH, DO, CODMn) collected from the Xiaofu River and compared with the results of a single LSTM. During the validation period, the R2 values when using LSTM for NH3-N, pH, DO and CODMn were 0.567, 0.657, 0.817 and 0.693, respectively, and the R2 values when using EEMD–LSTM for NH3-N, pH, DO and CODMn were 0.924, 0.965, 0.961 and 0.936, respectively. The results show that the developed model outperforms the single LSTM model in various evaluation indicators and greatly improves the model performance in terms of the hysteresis problem. The EEMD–LSTM model has high prediction accuracy and strong generalization ability, and further development may be valuable.

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Section: Study Areamentioning

confidence: 99%

Ensemble Empirical Mode Decomposition and a Long Short-Term Memory Neural Network for Surface Water Quality Prediction of the Xiaofu River, China

Luo

Zhang

Dong

et al. 2023

Water

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Impact of urbanization on baseflow characteristics in the central catchment of North China Plain, China

Tan,

Xin,

Guo

et al. 2023

Journal of Hydrology: Regional Studies

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Application of unsupervised clustering model based on graph embedding in water environment

Fang,

Lyu,

Wang

et al. 2023

Sci Rep

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Surface water monitoring data has spatiotemporal characteristics, and water quality will change with time and space in different seasons and climates. Data of this nature brings challenges to clustering, especially in terms of obtaining the temporal and spatial characteristics of the data. Therefore, this paper proposes an improved TADW algorithm and names it RTADW to obtain the spatiotemporal characteristics of surface water monitoring points. We improve the feature matrix in TADW and input the original time series data and spatial information into the improved model to obtain the spatiotemporal feature vector. When the improved TADW model captures watershed information for clustering, it can simultaneously extract the temporal and spatial characteristics of surface water compared with other clustering algorithms such as the DTW algorithm. We applied the proposed method to multiple different monitoring sites in the Liaohe River Basin, analyzed the spatiotemporal regional distribution of surface water monitoring points. The results show that the improved feature extraction method can better capture the spatiotemporal feature information between surface water monitoring points. Therefore, this method can provide more potential information for cluster analysis of water environment monitoring, thereby providing a scientific basis for watershed zoning management.

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Water Environmental Functional Zoning at County Level and Environmental Contamination Carrying Capacity Accounting in the Mainstream of Xiaofu River

Cited by 4 publications

References 13 publications

Ensemble Empirical Mode Decomposition and a Long Short-Term Memory Neural Network for Surface Water Quality Prediction of the Xiaofu River, China

Ensemble Empirical Mode Decomposition and a Long Short-Term Memory Neural Network for Surface Water Quality Prediction of the Xiaofu River, China

Impact of urbanization on baseflow characteristics in the central catchment of North China Plain, China

Application of unsupervised clustering model based on graph embedding in water environment

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