As one of the main characteristics of atmospheric pollutants,PM_(2.5) severely affects human health and has received widespread attention in recent years.How to predict the variations of PM_(2.5) concentrations with h...As one of the main characteristics of atmospheric pollutants,PM_(2.5) severely affects human health and has received widespread attention in recent years.How to predict the variations of PM_(2.5) concentrations with high accuracy is an important topic.The PM_(2.5) monitoring stations in Xinjiang Uygur Autonomous Region,China,are unevenly distributed,which makes it challenging to conduct comprehensive analyses and predictions.Therefore,this study primarily addresses the limitations mentioned above and the poor generalization ability of PM_(2.5) concentration prediction models across different monitoring stations.We chose the northern slope of the Tianshan Mountains as the study area and took the January−December in 2019 as the research period.On the basis of data from 21 PM_(2.5) monitoring stations as well as meteorological data(temperature,instantaneous wind speed,and pressure),we developed an improved model,namely GCN−TCN−AR(where GCN is the graph convolution network,TCN is the temporal convolutional network,and AR is the autoregression),for predicting PM_(2.5) concentrations on the northern slope of the Tianshan Mountains.The GCN−TCN−AR model is composed of an improved GCN model,a TCN model,and an AR model.The results revealed that the R2 values predicted by the GCN−TCN−AR model at the four monitoring stations(Urumqi,Wujiaqu,Shihezi,and Changji)were 0.93,0.91,0.93,and 0.92,respectively,and the RMSE(root mean square error)values were 6.85,7.52,7.01,and 7.28μg/m^(3),respectively.The performance of the GCN−TCN−AR model was also compared with the currently neural network models,including the GCN−TCN,GCN,TCN,Support Vector Regression(SVR),and AR.The GCN−TCN−AR outperformed the other current neural network models,with high prediction accuracy and good stability,making it especially suitable for the predictions of PM_(2.5)concentrations.This study revealed the significant spatiotemporal variations of PM_(2.5)concentrations.First,the PM_(2.5) concentrations exhibited clear seasonal fluctuations,with higher levels typically observed in winter and differences presented between months.Second,the spatial distribution analysis revealed that cities such as Urumqi and Wujiaqu have high PM_(2.5) concentrations,with a noticeable geographical clustering of pollutions.Understanding the variations in PM_(2.5) concentrations is highly important for the sustainable development of ecological environment in arid areas.展开更多
对水质情况进行准确评估和预测对水污染防控至关重要,然而,由于水质受多种因素的影响,其时间序列数据常常具有趋势性、季节性和长期依赖关系,传统的预测方法往往无法很好地捕捉这些特征。为了解决这些问题,首先基于STL(Seasonal and Tre...对水质情况进行准确评估和预测对水污染防控至关重要,然而,由于水质受多种因素的影响,其时间序列数据常常具有趋势性、季节性和长期依赖关系,传统的预测方法往往无法很好地捕捉这些特征。为了解决这些问题,首先基于STL(Seasonal and Trend Decomposition using Loess)和TCN(Temporal Convolutional Network)构建STL-TCN水质预测模型。其中,通过STL模型对水质时间序列数据进行趋势和季节性分解,有效地提取时序数据的周期性特征;利用TCN模型中并行结构和残差连接有效捕捉时间序列数据的长期依赖关系,对分解后的数据进行多步预测。然后,选用福建省浪石断面河流的氨氮数据来验证STL-TCN水质预测模型的预测效果,并与基于长短时记忆网络(LSTM)和门控循环单元结构(GRU)的水质预测模型进行对比实验。实验结果表明,STL-TCN水质预测模型12步预测的MAE平均值达到0.0343、RMSE平均值达到0.0494、R^(2)平均值达到0.94737,相对LSTM和GRU,MAE平均提高7.8%和8.1%、RMSE平均提高2.2%和1.8%、R^(2)平均提高7.9%和7.8%。说明STL-TCN水质预测模型能够有效提高水质预测的准确性和稳定性,可以作为辅助水环境管理和决策的一种有效手段。展开更多
基金supported by the Program of Support Xinjiang by Technology(2024E02028,B2-2024-0359)Xinjiang Tianchi Talent Program of 2024,the Foundation of Chinese Academy of Sciences(B2-2023-0239)the Youth Foundation of Shandong Natural Science(ZR2023QD070).
文摘As one of the main characteristics of atmospheric pollutants,PM_(2.5) severely affects human health and has received widespread attention in recent years.How to predict the variations of PM_(2.5) concentrations with high accuracy is an important topic.The PM_(2.5) monitoring stations in Xinjiang Uygur Autonomous Region,China,are unevenly distributed,which makes it challenging to conduct comprehensive analyses and predictions.Therefore,this study primarily addresses the limitations mentioned above and the poor generalization ability of PM_(2.5) concentration prediction models across different monitoring stations.We chose the northern slope of the Tianshan Mountains as the study area and took the January−December in 2019 as the research period.On the basis of data from 21 PM_(2.5) monitoring stations as well as meteorological data(temperature,instantaneous wind speed,and pressure),we developed an improved model,namely GCN−TCN−AR(where GCN is the graph convolution network,TCN is the temporal convolutional network,and AR is the autoregression),for predicting PM_(2.5) concentrations on the northern slope of the Tianshan Mountains.The GCN−TCN−AR model is composed of an improved GCN model,a TCN model,and an AR model.The results revealed that the R2 values predicted by the GCN−TCN−AR model at the four monitoring stations(Urumqi,Wujiaqu,Shihezi,and Changji)were 0.93,0.91,0.93,and 0.92,respectively,and the RMSE(root mean square error)values were 6.85,7.52,7.01,and 7.28μg/m^(3),respectively.The performance of the GCN−TCN−AR model was also compared with the currently neural network models,including the GCN−TCN,GCN,TCN,Support Vector Regression(SVR),and AR.The GCN−TCN−AR outperformed the other current neural network models,with high prediction accuracy and good stability,making it especially suitable for the predictions of PM_(2.5)concentrations.This study revealed the significant spatiotemporal variations of PM_(2.5)concentrations.First,the PM_(2.5) concentrations exhibited clear seasonal fluctuations,with higher levels typically observed in winter and differences presented between months.Second,the spatial distribution analysis revealed that cities such as Urumqi and Wujiaqu have high PM_(2.5) concentrations,with a noticeable geographical clustering of pollutions.Understanding the variations in PM_(2.5) concentrations is highly important for the sustainable development of ecological environment in arid areas.
文摘对水质情况进行准确评估和预测对水污染防控至关重要,然而,由于水质受多种因素的影响,其时间序列数据常常具有趋势性、季节性和长期依赖关系,传统的预测方法往往无法很好地捕捉这些特征。为了解决这些问题,首先基于STL(Seasonal and Trend Decomposition using Loess)和TCN(Temporal Convolutional Network)构建STL-TCN水质预测模型。其中,通过STL模型对水质时间序列数据进行趋势和季节性分解,有效地提取时序数据的周期性特征;利用TCN模型中并行结构和残差连接有效捕捉时间序列数据的长期依赖关系,对分解后的数据进行多步预测。然后,选用福建省浪石断面河流的氨氮数据来验证STL-TCN水质预测模型的预测效果,并与基于长短时记忆网络(LSTM)和门控循环单元结构(GRU)的水质预测模型进行对比实验。实验结果表明,STL-TCN水质预测模型12步预测的MAE平均值达到0.0343、RMSE平均值达到0.0494、R^(2)平均值达到0.94737,相对LSTM和GRU,MAE平均提高7.8%和8.1%、RMSE平均提高2.2%和1.8%、R^(2)平均提高7.9%和7.8%。说明STL-TCN水质预测模型能够有效提高水质预测的准确性和稳定性,可以作为辅助水环境管理和决策的一种有效手段。
