Accurate prediction of water quality is conducive to intelligent management and control of watershed ecology. Water quality data has time series characteristics, and although methods such as LSTM can capture sequence correlations in time series data, these methods do not consider the impact of bidirectional neighborhoods on the model, and they are not able to pay attention to the feature sequences to varying degrees. Aiming at this problem, this paper proposes a watershed water quality prediction model based on attention mechanism and bidirectional LSTM neural network (AT-BILSTM). The model mainly contains the Bi-LSTM layer and the temporal attention layer, and the attention mechanism is introduced after the bidirectional feature extraction of the water quality time series data to highlight the data series that have a key impact on the prediction results, and effectively use the sequence correlation of the watershed water quality data to improve the accuracy of the model. Finally, the actual datasets of four monitoring sites in the Lanzhou section of the Yellow River Basin in China were used to verify the effectiveness of the method. After comparing the reference model, the results show that the proposed model combines the bidirectional nonlinear mapping ability of Bi-LSTM and the feature-weighted characteristics of the attention mechanism. Taking FuHe Bridge as an example, compared with the original LSTM model, the RMSE and MAE of the model are reduced to 0.101 and 0.059, respectively, and the R2 is increased to 0.970, which has the best prediction performance in four sections, which can provide decision-making basis for comprehensive water quality management and pollutant control in the river basin.
Spatially explicit urban air quality information is important for urban fine-management and public life. However, existing air quality measurement methods still have some limitations on spatial coverage and system stability. A micro station is an emerging monitoring system with multiple sensors, which can be deployed to provide dense air quality monitoring data. Here, we proposed a method for urban air quality mapping at high-resolution for multiple pollutants. By using the dense air quality monitoring data from 448 micro stations in Lanzhou city, we developed a decision tree model to infer the distribution of citywide air quality at a 500 m × 500 m × 1 h resolution, with a coefficient of determination (R2) value of 0.740 for PM2.5, 0.754 for CO and 0.716 for SO2. Meanwhile, we also show that the deployment density of the monitoring stations can have a significant impact on the air quality inference results. Our method is able to show both short-term and long-term distribution of multiple important pollutants in the city, which demonstrates the potential and feasibility of dense monitoring data combined with advanced data science methods to support urban atmospheric environment fine-management, policy making, and public health studies.
Water quality prediction is a fundamental and necessary task for the prevention and management of water environment pollution. Due to the fluidity of water, different sections of the same river have similar trends in their water quality. The present water quality prediction methods cannot exploit the correlation between the water quality of each section to deeply capture information because they do not take into account how similar the water quality is between sections. In order to address this issue, this paper constructs a water quality prediction model based on multi-task deep learning, taking the chemical oxygen demand (COD) of the water environment of the Lanzhou portion of the Yellow River as the research object. The multiple sections of correlation are trained and learned in this model at the same time, and the water quality information of each section is shared while retaining their respective heterogeneity, and the hybrid model CNN-LSTM is used for better mining from local to full time series features of water quality information. In comparison to the current single-section water quality prediction, experiments have shown that the model’s mean absolute error (MSE) and root mean square error (RMSE) of the predicted value of the model are decreased by 13.2% and 15.5%, respectively, and that it performs better in terms of time stability and generalization.
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