A balanced social LSTM for PM2.5 concentration prediction based on local spatiotemporal correlation

Shi, Lukui; Zhang, Huizhen; Xu, Xia; Han, Ming; Zuo, Peiliang

doi:10.1016/j.chemosphere.2021.133124

Cited by 27 publications

(12 citation statements)

References 35 publications

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“…Compared with the three optimization models, although the prediction error of LSTM is larger, its MAE mean is less than 0.4. For general time series problems, it is also a better choice, [58][59][60][61] which also shows that LSTM is suitable for the prediction of comfort perception at different moments in the sports state.…”

Section: Model Error Analysismentioning

confidence: 99%

Intelligent research on wearing comfort of tight sportswear during exercise

Cheng

Wang

Zeng

et al. 2022

Journal of Industrial Textiles

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In this study, the distribution characteristics and changing law of sports comfort perception were analyzed by collecting the comfort evaluation data of running in winter tight sportswear, and proposes a network model based on particle swarm optimization-cuckoo search-long short-term memory to track the changing law of motion comfort. First, considering the existence of redundant features, analytic hierarchy process analysis is used to screen out key features; and then, particle swarm optimization and cuckoo search algorithms are used to optimize the key parameters of the long short-term memory prediction model, so as to avoid the model prediction performance caused by the selection of parameters based on experience. The experiments compared the prediction accuracy of other models, and selected mean absolute error, root mean square error, and mean absolute percentage error evaluation indicators to verify the effectiveness of these models. The results show that the perception of wearing comfort changes over time, but when it reaches the extreme point at a certain moment, and then it gradually falls back. The humidity sense and thermal sense of bust, crotch, and back in human body are the main comfort perceptions that affect movement; LSTM and the optimized LSTM models are suitable for the prediction of comfort perception at different times during exercise. Among them, the PSO-CS-LSTM model can more accurately track the changing trend of motion comfort, the prediction has high prediction accuracy and validity; we selected three different running speeds as the experimental data, which also verifies the universal applicability of the model.

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Section: Model Error Analysismentioning

confidence: 99%

Intelligent research on wearing comfort of tight sportswear during exercise

Cheng

Wang

Zeng

et al. 2022

Journal of Industrial Textiles

View full text Add to dashboard Cite

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“…The RNN proposed by Hopfield can model time series data and extract the time dependence of context [ 25 ]. Subsequently, the RNN variant model LSTM [ 26 , 27 , 28 , 29 , 30 , 31 ] and GRU [ 32 , 33 , 34 ] proposed to solve the short-term memory problem caused by the disappearance of the RNN gradient. Zhang et al apply the ConvLSTM model to model the data of sky stations and daily aerosol optical thickness to predict the daily spatial distribution of PM 2.5 concentration [ 35 ].…”

Section: Related Workmentioning

confidence: 99%

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

Lin

Zhao

et al. 2022

Entropy

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Accurate and fine-grained prediction of PM2.5 concentration is of great significance for air quality control and human physical and mental health. Traditional approaches, such as time series, recurrent neural networks (RNNs) or graph convolutional networks (GCNs), cannot effectively integrate spatial–temporal and meteorological factors and manage dynamic edge relationships among scattered monitoring stations. In this paper, a spatial–temporal causal convolution network framework, ST-CCN-PM2.5, is proposed. Both the spatial effects of multi-source air pollutants and meteorological factors are considered via spatial attention mechanism. Time-dependent features in causal convolution networks are extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-PM2.5 are tuned by Bayesian optimization. Haikou air monitoring station data are employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results include the following points: (1) For a single station, the RMSE, MAE and R2 values of ST-CCN-PM2.5 decreased by 27.05%, 10.38% and 3.56% on average, respectively. (2) For all stations, ST-CCN-PM2.5 achieve the best performance in win–tie–loss experiments. The numbers of winning stations are 68, 63, and 64 out of 95 stations in RMSE (MSE), MAE, and R2, respectively. In addition, the mean MSE, RMSE and MAE of ST-CCN-PM2.5 are 4.94, 2.17 and 1.31, respectively, and the R2 value is 0.92. (3) Shapley analysis shows wind speed is the most influencing factor in fine-grained PM2.5 concentration prediction. The effects of CO and temperature on PM2.5 prediction are moderately significant. Friedman test under different resampling further confirms the advantage of ST-CCN-PM2.5. The ST-CCN-PM2.5 provides a promising direction for fine-grained PM2.5 prediction.

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“…In order to evaluate the pollution degree of the environment, the concentration of factors that caused pollution must be identified [ 17 , 18 , 19 ]. The groundwater quality index (GWQI) and irrigation water quality index (IWQI) can evaluate groundwater quality for drinking and irrigation purposes [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Liu

et al. 2022

IJERPH

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Prediction of groundwater quality is an essential step for sustainable utilization of water resources. Most of the related research in the study area focuses on water distribution and rational utilization of resources but lacks results on groundwater quality prediction. Therefore, this paper introduces a prediction model of groundwater quality based on a long short-term memory (LSTM) neural network. Based on groundwater monitoring data from October 2000 to October 2014, five indicators were screened as research objects: TDS, fluoride, nitrate, phosphate, and metasilicate. Considering the seasonality of water quality time series data, the LSTM neural network model was used to predict the groundwater index concentrations in the dry and rainy periods. The results suggest the model has high accuracy and can be used to predict groundwater quality. The mean absolute errors (MAEs) of these parameters are, respectively, 0.21, 0.20, 0.17, 0.17, and 0.20. The root mean square errors (RMSEs) are 0.31, 0.29, 0.28, 0.27, and 0.31, respectively. People can be given early warnings and take measures according to the forecast situation. It provides a reference for groundwater management and sustainable utilization in the study area in the future and also provides a new idea for coastal cities with similar hydrogeological conditions.

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A balanced social LSTM for PM2.5 concentration prediction based on local spatiotemporal correlation

Cited by 27 publications

References 35 publications

Intelligent research on wearing comfort of tight sportswear during exercise

Intelligent research on wearing comfort of tight sportswear during exercise

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

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