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| 1 | 基于门控循环单元和图神经网络的PM2.5预测显示文摘PM2.5浓度指数是衡量空气质量的重要指标之一,但由于PM2.5数据的非线性以及受多种气象因素的影响,因此实现精确预测较为困难。本文采用门控循环单元和图神经网络相结合的混合模型预测的方式,并进一步采用改进的门控循环单元提升网络效果。通过对门控循环单元的输入信息与隐藏层信息进行数据交互增强上下文信息联系,使得门控循环单元模块的转移函数依赖于信息上下文。一系列实验结果表明:提出的改进方法相比于现有的方法具有更好的性能以及泛化效果,在中国生态环境部提出的京津冀地区真实数据集上验证了方法的有效性,与现有网络相比预测准确率更高。 | 曹旺 王彤彤 张静怡 | 2022 | 现代计算机2022，28，5： | 2 |
| 2 | A comparison between artificial neural network algorithms and empirical equations applied to submerged weir scour evolution prediction显示文摘Estimating the time evolution of a local scour hole downstream of submerged weirs can help determine the maximum scour depth and length and is essential to designing submerged weir foundations.In the current study,artificial neural networks with a backpropagation learning algorithmwere used to estimate the temporal variation of scour profiles downstream of submerged weirs under clear water conditions.Physical factors,such as the flow condition,weir size,and sediment characteristics,are general scour considerations.Two sets of data combinations,namely original and non-dimensional data,were utilized in developing the backpropagation network(BPN)model.Using the data combinations and a trial-and-error method,the appropriate number of hidden neurons in the model architecture was determined.The results indicated that using non-dimensional variables as inputs in estimating the time evolution of scour holes downstream of a submerged weir is more suitable for the BPN model.Comparisons of the prediction results of the BPN model and an empirical regression formula revealed that the BPN model was more accurate and more direct in predicting the temporal variation of the local scour hole profile.In addition,sensitivity analysis showed that the dimensionless time exerted the most significant impact on the time evolution of the non-dimensional scour hole profile. | Dawei Guan Jingang Liu Yee-Meng Chiew Jian-Hao Hong Liang Cheng | 2023 | International Journal of Sediment Research2023，38，1： | 0 |
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