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Water quality predictions have a great importance in water resource managements and water pollution protections. Most the currently used water quality models can only predict time sequence of fixed length and ignore the real hydrological flow information. In this paper, we propose a new water quality prediction framework GTV-STP that adopts an embedding-Encoder-Decoder structure, in which the timestamps are introduced into the water quality information. The spatial embedding method is developed to introduce the spatial hydrological features into the water quality information, in order to achieve multisite parallel predictions. In addition, a variablelength decoder is put forward for the variable-length sequencial predictions. The practical water quality predictions of WT, pH, DO, COD Mn , NH 3 -N, P and N for Taihu Lake with GTV-STP framework are performed on two datasets of 6to12 in smaller-scale and 12to24 in larger-scale. Using the Huber Loss to balance the MAE and the RMSE, the val-Huber Loss of the GTV-STP framework is used to compare with that of the baseline models such as LSTM-CNN-ATT, LSTMNet and MLP in predicting accuracy. Results show that the GTV-STP framework has the highest accuracy with the val-Huber Loss of 0.079183 on 6to12 and 0.033561 on 12to24. It is shown that the GTV-STP framework has highly accurate not only for smaller-scale water quality predictions but also suitable for larger-scale predictions. In future, water quality predictions using other more precise frameworks containing spatial and time series information under deep learning will be one of the research directions.INDEX TERMS GTV-STP, spatial and temporal water quality information, Taihu Lake, variable length sequential predictions, water quality predictions.
Water quality predictions have a great importance in water resource managements and water pollution protections. Most the currently used water quality models can only predict time sequence of fixed length and ignore the real hydrological flow information. In this paper, we propose a new water quality prediction framework GTV-STP that adopts an embedding-Encoder-Decoder structure, in which the timestamps are introduced into the water quality information. The spatial embedding method is developed to introduce the spatial hydrological features into the water quality information, in order to achieve multisite parallel predictions. In addition, a variablelength decoder is put forward for the variable-length sequencial predictions. The practical water quality predictions of WT, pH, DO, COD Mn , NH 3 -N, P and N for Taihu Lake with GTV-STP framework are performed on two datasets of 6to12 in smaller-scale and 12to24 in larger-scale. Using the Huber Loss to balance the MAE and the RMSE, the val-Huber Loss of the GTV-STP framework is used to compare with that of the baseline models such as LSTM-CNN-ATT, LSTMNet and MLP in predicting accuracy. Results show that the GTV-STP framework has the highest accuracy with the val-Huber Loss of 0.079183 on 6to12 and 0.033561 on 12to24. It is shown that the GTV-STP framework has highly accurate not only for smaller-scale water quality predictions but also suitable for larger-scale predictions. In future, water quality predictions using other more precise frameworks containing spatial and time series information under deep learning will be one of the research directions.INDEX TERMS GTV-STP, spatial and temporal water quality information, Taihu Lake, variable length sequential predictions, water quality predictions.
Limited water quality data is often responsible for incorrect model description and misleading interpretation in water resources planning and management scenarios. This study compares two hybrid strategies to convert discrete concentration data into continuous daily values for one year in different river sections. Model A is based on an autoregressive process, accounting for serial correlation, water quality historical characteristics (mean and standard deviation) and random variability; the second approach (model B) is a regression model, based on the relationship between monitoring flow and concentrations, plus an error term. The generated series (here referred to as synthetic series) are propagated in time and space by a full deterministic model (SihQual), that solves the Saint-Venant and advection-dispersion-reaction equations. Results reveal that both approaches are appropriate to reproduce the variability of biochemical oxygen demand and organic nitrogen concentrations, leading to the conclusion that the combination of deterministic/empirical and stochastic components are compatible. A second outcome arises from the comparison of results in different time scales, supporting the need for further assessment of statistical characteristics of water quality data - which relies on monitoring plans. Nonetheless, the proposed methods are suitable to estimate multiple scenarios of interest in water resources planning and management.
Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.
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