本文旨在通过构建EEMD-GWO-LSTM混合模型,对兰州市空气质量指数(AQI)进行准确预测。兰州市作为中国西北地区重要的工业基地和交通枢纽,其空气质量受工业排放、交通污染及地理环境等因素影响,常年处于高污染等级。针对兰州市AQI监测数据...本文旨在通过构建EEMD-GWO-LSTM混合模型,对兰州市空气质量指数(AQI)进行准确预测。兰州市作为中国西北地区重要的工业基地和交通枢纽,其空气质量受工业排放、交通污染及地理环境等因素影响,常年处于高污染等级。针对兰州市AQI监测数据突变性强的特点,文章首先对数据进行预处理,包括填补缺失值、归一化处理等,以提高数据质量。随后,采用集合经验模态分解(EEMD)对数据进行分解,提取出本征模态函数(IMF),并利用灰狼优化算法(GWO)对长短期记忆网络(LSTM)模型的超参数进行优化,以提高预测精度。实验结果表明,EEMD-GWO-LSTM混合模型在预测兰州市AQI时,相较于单一模型和其他混合模型,具有更低的均方根误差(RMSE)和更高的决定系数(R2),显示出更好的预测性能。最后,文章提出了增加监测站点、采用先进技术提高监测频率、跨区域合作及数据公开共享等建议,以促进兰州市空气质量的持续改善和预测模型的进一步优化。This paper aims to accurately predict the Air Quality Index (AQI) of Lanzhou City by constructing a mixed model of EEMD-GGO-LSTM. Lanzhou City is an important industrial base and transportation hub in northwest China. Its air quality is affected by industrial emissions, traffic pollution, and the geographical environment, and it always has a high pollution level. In view of the strong mutability of AQI monitoring data in Lanzhou City, the paper first preprocessed the data, including filling in missing values and normalization processing, so as to improve the data quality. Then, the data is decomposed by ensemble empirical Mode decomposition (EEMD), extracting the intrinsic mode function (IMF), and the hyperparameters of the long short-term memory network (LSTM) model are optimized by Grey Wolf optimization algorithm (GWO) to improve the prediction accuracy. The experimental results show that the EEMD-GGO-LSTM mixed model has a lower root-mean-square error (RMSE) and higher determination coefficient (R2) when predicting AQI in Lanzhou compared with the single model and other mixed models, showing better prediction performance. Finally, the paper puts forward some suggestions, such as increasing monitoring stations, using advanced technology to improve monitoring frequency, cross-regional cooperation and open data sharing, so as to promote the continuous improvement of Lanzhou air quality and further optimization of a prediction model.展开更多
为研究不同群体柔鱼(Ommastrephes bartramii)生长的异质性,文章根据2016—2018年在北太平洋采集的柔鱼样本,对该物种胴长和体质量的性别和月间差异进行了分析。构建了广义线性模型(Generalized linear model,GLM)和9个线性混合模型(Lin...为研究不同群体柔鱼(Ommastrephes bartramii)生长的异质性,文章根据2016—2018年在北太平洋采集的柔鱼样本,对该物种胴长和体质量的性别和月间差异进行了分析。构建了广义线性模型(Generalized linear model,GLM)和9个线性混合模型(Linear mixed model,LMM),用于研究不同群体柔鱼的胴长和体质量关系(m=aLb)在性别及时间上的差异。结果显示,东、西部群体不同性别柔鱼胴长、体质量均存在极显著差异(P<0.01);各月份间也均存在极显著差异(P<0.01)。东部群体异速生长参数(b)的固定值(2.7634)小于3,表示其为负异速增长,肥满度随胴长增加而减少,胴体趋于细长;西部群体b的固定值(3.11719)大于3,表示其为正异速增长,肥满度随胴长增加而增大,胴体趋于宽厚。研究表明,性别和月份对不同群体柔鱼胴长和体质量关系均有显著影响,线性混合模型能将性别和月份的异质性通过随机效应在单个模型中更准确、方便地体现出来,进一步证实了此模型在个体生长异质性研究中的优势。展开更多
文摘本文旨在通过构建EEMD-GWO-LSTM混合模型,对兰州市空气质量指数(AQI)进行准确预测。兰州市作为中国西北地区重要的工业基地和交通枢纽,其空气质量受工业排放、交通污染及地理环境等因素影响,常年处于高污染等级。针对兰州市AQI监测数据突变性强的特点,文章首先对数据进行预处理,包括填补缺失值、归一化处理等,以提高数据质量。随后,采用集合经验模态分解(EEMD)对数据进行分解,提取出本征模态函数(IMF),并利用灰狼优化算法(GWO)对长短期记忆网络(LSTM)模型的超参数进行优化,以提高预测精度。实验结果表明,EEMD-GWO-LSTM混合模型在预测兰州市AQI时,相较于单一模型和其他混合模型,具有更低的均方根误差(RMSE)和更高的决定系数(R2),显示出更好的预测性能。最后,文章提出了增加监测站点、采用先进技术提高监测频率、跨区域合作及数据公开共享等建议,以促进兰州市空气质量的持续改善和预测模型的进一步优化。This paper aims to accurately predict the Air Quality Index (AQI) of Lanzhou City by constructing a mixed model of EEMD-GGO-LSTM. Lanzhou City is an important industrial base and transportation hub in northwest China. Its air quality is affected by industrial emissions, traffic pollution, and the geographical environment, and it always has a high pollution level. In view of the strong mutability of AQI monitoring data in Lanzhou City, the paper first preprocessed the data, including filling in missing values and normalization processing, so as to improve the data quality. Then, the data is decomposed by ensemble empirical Mode decomposition (EEMD), extracting the intrinsic mode function (IMF), and the hyperparameters of the long short-term memory network (LSTM) model are optimized by Grey Wolf optimization algorithm (GWO) to improve the prediction accuracy. The experimental results show that the EEMD-GGO-LSTM mixed model has a lower root-mean-square error (RMSE) and higher determination coefficient (R2) when predicting AQI in Lanzhou compared with the single model and other mixed models, showing better prediction performance. Finally, the paper puts forward some suggestions, such as increasing monitoring stations, using advanced technology to improve monitoring frequency, cross-regional cooperation and open data sharing, so as to promote the continuous improvement of Lanzhou air quality and further optimization of a prediction model.
