• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    A New Model Using Multiple Feature Clustering and Neural Networks for Forecasting Hourly PM2.5 Concentrations, and Its Applications in China

    2020-11-05 10:00:08HuiLiuZhihaoLongZhuDuanHuipengShi
    Engineering 2020年8期

    Hui Liu*, Zhihao Long, Zhu Duan, Huipeng Shi

    Institute of Artificial Intelligence and Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering,Central South University, Changsha 410075, China

    Keywords:PM2.5 concentrations forecasting PM2.5 concentrations clustering Empirical wavelet transform Multi-step forecasting

    ABSTRACT Particulate matter with an aerodynamic diameter no greater than 2.5 μm(PM2.5)concentration forecasting is desirable for air pollution early warning. This study proposes an improved hybrid model, named multi-feature clustering decomposition (MCD)-echo state network (ESN)-particle swarm optimization(PSO), for multi-step PM2.5 concentration forecasting. The proposed model includes decomposition and optimized forecasting components. In the decomposition component, an MCD method consisting of rough sets attribute reduction (RSAR), k-means clustering (KC), and the empirical wavelet transform(EWT) is proposed for feature selection and data classification. Within the MCD, the RSAR algorithm is adopted to select significant air pollutant variables, which are then clustered by the KC algorithm. The clustered results of the PM2.5 concentration series are decomposed into several sublayers by the EWT algorithm.In the optimized forecasting component,an ESN-based predictor is built for each decomposed sublayer to complete the multi-step forecasting computation. The PSO algorithm is utilized to optimize the initial parameters of the ESN-based predictor. Real PM2.5 concentration data from four cities located in different zones in China are utilized to verify the effectiveness of the proposed model.The experimental results indicate that the proposed forecasting model is suitable for the multi-step high-precision forecasting of PM2.5 concentrations and has better performance than the benchmark models.

    1. Introduction

    With the growth of urban industry in developing countries and regions,air pollution has become a difficult issue that is attracting attention all over the world. In recent years, hazy weather has appeared in most regions of China, and air quality has become a national strategic problem. Particulate matter with an aerodynamic diameter no greater than 2.5 μm (PM2.5) contains a large number of toxic and harmful substances [1]. PM2.5is the most common air pollutant and has a negative impact on human health and air quality [2]. Previous studies have shown that PM2.5pollution has a direct impact on the respiratory and cardiovascular systems, and is closely related to the incidence and mortality of lung cancer [3]. In addition, PM2.5has a bad influence on the weather and climate. For example, PM2.5may cause abnormal rainfall and aggravate the greenhouse effect [4-7].

    Given the serious negative impact of PM2.5on people’s lives,increasing attention is being paid to PM2.5concentration forecasting. PM2.5concentration forecasting is considered to be an important and effective method for alleviating the negative effect of PM2.5[8]. This method is also important for applications of urban big data in the development of smart cities [9].

    1.1. Related works

    PM2.5concentration forecasting methods can be divided into four types: physical models, statistical models, artificial intelligence models, and hybrid models.

    Physical models focus on understanding the potentially complex emissions,transport,and conversion processes of meteorological and chemical factors[10].The physical method yields accurate prediction results. However, physical models require sufficient emission information on air pollution [11] and their calculation cost is high [12]. Statistical models overcome the disadvantage of physical methods, as they require simple samples and have a fast calculation speed [13]. However, statistical models do not sufficiently consider the covariance among various influential factors,because they are generally based on limited samples.A single artificial intelligence model can describe the rule of the nonlinear system and has great advantages in dealing with big data [14].However, the disadvantage of such a model lies in the calculation costs of the neural network,which are greater than those of a statistical model. Moreover, the training process of the neural network has a certain volatility, so its output may not be the optimal result [15].

    Considering the limitations of the above methods,hybrid models have been widely used in air pollution prediction.Hybrid models usually combine three parts: data preprocessing, feature selection,and a predictor.Data preprocessing can sort out complex data relationships in the original data and make it more stationary.Feature selection can improve the input data structure and reduce the difficulty of model training caused by a too-high dimension.Hybrid models can integrate the advantages of each algorithm to achieve better model performance. Many related works have shown that hybrid models tend to have better predictive performance [16-20]. Table 1 [16-28] lists some cutting-edge research on hybrid PM2.5concentration forecasting to better illustrate the application of hybrid methods in PM2.5concentration forecasting.

    Feature selection is rarely used in the current hybrid PM2.5concentration forecasting models listed in Table 1. However, if the input of a PM2.5concentration forecasting model includes many features, such as PM2.5, PM10, sulfur dioxide (SO2), and ozone(O3),it may cause difficulties in the PM2.5concentration forecasting model training and increase the training time.This also affects the robustness of the PM2.5concentration forecasting model [29]. At the same time, complex input data may lead to overfitting of the model and may reduce the accuracy of the model [30]. At present,common feature selection algorithms include the principal components analysis (PCA), phase space reconstruction (PSR), and gradient-boosted regression tree (GBRT). However, these methods may be unsuitable for air pollutant concentration sequences because they assume a linear system,which may lead to problems such as not achieving global optimal reduction. The rough sets attribute reduction(RSAR)algorithm,which is based on fuzzy theory, has the advantages of clear stop criteria and no parameters[31].RSAR can obtain the important attribute set of the target attribute through the dependency between different attributes. The RSAR algorithm is a hot research topic [32]. Clustering algorithms are commonly used in data mining and analysis[33].Various clustering methods exist, such as k-means clustering (KC) [34], possibilistic c-means (PCM) [35], cure clustering [36], and so forth.Compared with others, the KC algorithm has the advantages of a simple principle, fast computing speed, and excellent clustering results; thus, the KC algorithm is the most widely used clustering algorithm at present. Combining the RSAR algorithm and the KC algorithm makes it possible to use RSAR to provide reasonable clustering objects for the KC algorithm, which is a valuable research point.

    Decomposition mainly focuses on the wavelet theory method in Table 1. The decomposition algorithm can divide the original data into a series of more stable sublayers according to the different time scales. Compared with empirical mode decomposition(EMD), ensemble empirical mode decomposition (EEMD), and complex empirical mode decomposition (CEMD), the empirical wavelet transform (EWT) algorithm can adaptively divide the Fourier spectrum and select the appropriate wavelet filter banks[37]. The clustering method can also be employed for decomposition in PM2.5concentration forecasting fields. The clustering algorithm can classify the original data according to different air pollutant scenarios.The influence of sample diversity on the model training can be reduced by clustering. However, few studies use aclustering algorithm with a decomposition algorithm in hybrid PM2.5concentration forecasting.

    Table 1 Main studies on PM2.5 concentration forecasting in the past four years.

    The predictors shown in Table 1 are commonly used physical methods, machine learning,and artificial neural networks(ANNs).Although the Copernicus Atmosphere Monitoring Service (CAMS),weather research and forecasting model with chemistry (WRFChem),and Nested Air Quality Prediction Model System(NAQPMS)have accurate prediction results,they require complex data and an understanding of a variety of physical and chemical relationships.Therefore,these methods require a great deal of preparatory work and a high level of professional knowledge. Support vector machines (SVMs), support vector regression (SVR), and least-squares support vector regression (LS-SVR) are very demanding in their choice of parameters,and cannot handle problems with large data.Traditional neural networks such as the backpropagation neural network (BPNN) and evolutionary neural network (ENN) need a great deal of training to build complex neural relationships, and are easy to overfit. The echo state network (ESN) has a unique reservoir structure that consists of recurrently connected units.As a result, the training process of the ESN is simple and effective,which is suitable for nonlinear systems such as PM2.5concentration data [38]. The ESN has been used in other fields such as wind speed prediction [38]. Therefore, application of the ESN model in hybrid PM2.5concentration forecasting is very appropriate.

    1.2. The innovation of this study

    To summarize the references described above, the decomposition-based clustering algorithm, nonlinear fuzzy theory algorithm,and ESN are rarely studied in PM2.5concentration forecasting.This study aims to apply these algorithms for hybrid PM2.5concentration forecasting. The proposed hybrid PM2.5prediction model combines three methods: multi-feature clustering decomposition (MCD), ESN, and particle swarm optimization (PSO). In MCD,the RSAR algorithm is adopted to select significant air pollutant concentrations. Then the KC algorithm is used to divide the original PM2.5concentration data into several groups according to the results of the RSAR algorithm. The clustered results for the PM2.5concentration series are automatically decomposed into several sublayers by the EWT algorithm. An ESN-based predictor is built for every decomposed sublayer in each clustered group to complete the multi-step forecasting computation. The forecasted results from every sublayer are then further integrated to form the final predicting values. The PSO algorithm is utilized to optimize the initial parameters of the ESN-based predictor.The experimental results show that the proposed hybrid model can accurately predict average hourly concentrations of PM2.5.The details of the proposed model are explained in Section 2.

    2. Methodology

    2.1. Framework of the proposed model

    The construction procedure of the hybrid MCD-ESN-PSO model is as follows:

    Part A: MCD

    This part consists of the RSAR,KC,and EWT algorithms.The raw air quality data are filtered by the RSAR algorithm,and the filtered attribute data are clustered by the KC algorithm. The raw data are divided into several clusters by this step.Then the clustered data of each cluster are decomposed into sublayers by the EWT algorithm.Finally,the sublayers are utilized to establish different ESN models.Through the MCD method, the RSAR algorithm and KC algorithm can act on the raw data together to achieve the clustering of features. Through the decomposition processing of the EWT algorithm, the original time series is finally decomposed into more and better sublayers. The details of the RSAR, KC, and EWT algorithms are introduced in Sections 2.2-2.4, respectively.

    Part B: ESN

    The ESN is a basic predictor, which forecasts the decomposed PM2.5concentration data. The ESN is composed of an input layer,reserve pool, and output layer. The main idea of the ESN is to use the reserve pool to simulate a complex dynamic space that can change with the input.Referring to Ref.[38],the updated equation and output state equation of the ESN can be expressed as Eqs. (1)and (2):

    where x is input data from reserve pool to output layer;y is output;t is time; u is input data from input layer to reserve pool; f is the function of ESN; Winrepresents the connection weights of x(t - 1)to x(t); u (t + 1) is the input data; Wbackrepresents the connection weights of the input layer to the reserve pool; and Woutrepresents the connection weights of y(t - 1) to x(t).

    Part C: PSO

    Unlike the traditional ESN model, the ESN model proposed in this paper is combined with the PSO algorithm. In the ESN-PSO algorithm,the relevant parameters of the ESN model,such as input scaling,spectral radius,internal unit number,and connectivity,are optimized by the PSO algorithm.

    Finally, the forecasting results of each sublayer are combined with the corresponding original sublayer. The prediction results of each sublayer are added to obtain the final prediction results.

    2.2. Rough sets attribute reduction

    RSAR can be used to remove useless information while maintaining the quality of the sorting of the existing information [31].The obtained information is referred to as‘‘reducts.”In an information system, a set of objects are described by a set of attributes[31]. A knowledge information system is defined as follows:

    where U is a finite nonempty set of objects; V is a nonempty set of values;A is a finite nonempty set of attributes;and h is an information function that maps an object in U to exactly one value in V.

    In this study, A is a set of all attributes such as A = {PM10, CO,SO2,NO2,O3,PM2.5},and V is their value.f is the dependency function that is used to obtain γ, and γ is the dependence of the set,which is calculated in the process of RSAR.

    The reduct should maintain the quality of sorting(γ),defining a condition attributes set C ?A and an attribute set P ?C ?A.Sometimes, an information table may have more than one reduct; the intersection of all the reducts is called a ‘‘core” of a decisionmaking table and is expressed as core (P); this is the most important attributes set for an information system.

    2.3. k-means clustering

    KC is a simple iterative clustering algorithm,using distance as a similarity index [34]. Its final purpose is to find k clusters in a group of given datasets. The center of each cluster is according to the value of all clusters in which each cluster is described by the clustering center. The process of the KC algorithm is as follows:

    (1) Select the k object in the data space as the initial center;each object represents a cluster center.

    (2)Divide the data objects in the sample into the corresponding classes according to the nearest clustering center,according to the Euclidean distance between them and these cluster centers.

    where xiis the ith sample in the jth cluster;xjis the center of the jth cluster; and D represents the number of attributes of a data object.

    (3) Update the clustering center: taking the mean values of all objects in each cluster as the clustering center, calculating the value of the objective function.

    (4)Judge whether the values of the cluster centers and objective functions are equal. If they are equal, output results; otherwise,return to step (2).

    2.4. Empirical wavelet transform

    In this paper,the EWT algorithm is used for data preprocessing.The EWT,proposed by Gilles[37],is a novel signal-processing technique that builds the wavelets adaptively.The EWT is based on the theoretical framework of wavelet transform but overcomes the shortage of EMD theory and the problem of signal aliasing. The EWT adaptively divides the Fourier spectrum and selects the appropriate wavelet filter banks. The empirical scaling function and the empirical wavelets can be expressed as Eqs.(5)and(6),respectively:

    where n is divided interval;ω is frequency;β is any function in the interval[0,1]that satisfies the derivative of the order,τ is frequency coefficient; β(x) = x4(35 - 84x + 70x2- 20x3) and τ <min n [(ωn+1-ωn)/ (ωn+1+ωn)].

    2.5. Particle swarm optimization

    The PSO algorithm consists of position z,speed v,and the adaptation function. Each particle in the algorithm represents a candidate solution in the solution space. The fitness function is set according to the optimization goal. During the training of the PSO, each particle in the algorithm updates its own position by combining its current movement experience with the movement experience of the neighboring particles. The solution is realized by putting one’s own position close to the target position.The calculation formula is as follows [27]:

    3. Case study

    3.1. Study area

    Related literature studies show that the distribution of PM2.5concentrations ranges widely in China. It is mainly concentrated in North China and Central China [39,40]. In order to ensure the diversity of experimental data, the selected data should include different working conditions such as serious PM2.5pollution and weak PM2.5pollution.Beijing in the North China Plain,Guangzhou in the Pearl River Delta, Changsha in Central China, and Suzhou in the Yangtze River Delta are typical cities that are used to verify the effectiveness of the proposed model.The selected samples are spatially representative and contain PM2.5concentration data under different geographic and climatic environments, which can well verify the effectiveness of the proposed model.

    Monitoring stations record the average concentrations of six kinds of air pollutants(PM2.5, PM10, NO2, SO2, O3, and CO). Fig. 1 shows the selected datasets and related introduction.

    3.2. Data description and partitioning

    Fig. 1. Locations of the air quality monitoring stations. (a) Beijing. Beijing is the capital of China, located at the north end of the North China Plain. It has a typical warm temperate semi-humid continental monsoon climate,hot and rainy in summer,cold and dry in winter,short in spring and autumn.The average annual temperature is 10-12 °C and the average annual rainfall is more than 600 mm. (b) Changsha. Changsha is an important city in the middle reaches of the Yangtze River. It is a subtropical monsoon climate, mild climate, abundant precipitation, hot and rainy at the same time. Its average annual temperature is 17.2 °C and the average annual precipitation is 1361.6 mm.(c)Guangzhou.Guangzhou is located in the southeastern part of China,the northern edge of the Pearl River Delta,and the Pearl River passes through the urban area.It belongs to the tropical monsoon climate with high temperature,rainfall,and low wind speed.(d)Suzhou.Suzhou is located in the southeast of Jiangsu Province and the middle of the Yangtze River Delta. It is a subtropical monsoon marine climate, four distinct seasons, abundant rainfall throughout the year. Group A: models without RSAR-KC, including the ESN, LSTM, ESN-PSO, and EWT-ESN-PSO model.

    Experimental data are collected from four cities: Beijing,Guangzhou, Changsha, and Suzhou. As Shi et al. [41] have indicated, the spatial representation of surface site observation is often 0.5-16 km2, with the most frequent values being around 3 km2.The data of a single monitoring station cannot represent the air quality of the whole city.Therefore,each set of data is the mean value of all air quality monitoring stations in the corresponding city,so that the samples can represent the air quality of the whole city. For convenience, these datasets are named D1 (Beijing), D2(Guangzhou), D3 (Changsha), and D4 (Suzhou). The length of the sample data is set to one year so that the data can cover a complete set of four seasons. In this study,the sample data in 2016 are randomly selected. All experimental data includes one-hour average concentrations of PM2.5,PM10,NO2,SO2,O3,and CO collected from 1 January 2016 to 31 December 2016. All data are retrieved from the website of the China National Environmental Monitoring Center.?? http://www.cnemc.cn/

    Missing value filtering and outlier checking are implemented before data partitioning.Through the sample set analysis,it is concluded that there are 220 missing pieces of data in dataset D1.Dataset D2 is missing 158 pieces of data, dataset D3 is missing 158 pieces of data, and dataset D4 is missing 157 pieces of data.Since the number of missing samples is less than 2.5% of the total sample set, direct elimination will not have a significant impact.Through visual inspection of the outliers in Fig. 1, it is found that the outliers are mostly concentrated from January to March and from October to December. In order to ensure the training effect of the model, outliers are regarded as normal and retained.

    After removing the missing samples, D1 has 8540 samples, D2 has 8602 samples,D3 has 8602 samples,and D4 has 8603 samples.The 4001st-4600th samples of each dataset(other data are utilized for KC in group B)are used to train the models in group A(models without RSAR-KC, including the ESN, long-short term memory network(LSTM),ESN-PSO,and EWT-ESN-PSO model),which only use PM2.5concentrations.The 4601st-5000th samples are the testing set;to ensure the prediction effect,the 4601st-4900th samples are abandoned.All of the experimental data of each station is used in the RSAR and KC to preprocess the data in group B(models with RSAR-KC, including the RSAR-KC-ESN, the MCD-LSTM-PSO, and the RSAR-KC-EWT-ESN-PSO model). To ensure the effectiveness of error evaluating, each cluster is used to train an ESN model and then reconstruct the predicted results for the 4901st-5000th samples.

    In order to study the influence of different sampling processes on model accuracy, the 3001st-4000th (S1) sample points and 6001st-7000th (S2) sample points in D1 are used for comparison experiments. Fig. 2 shows the distribution of datasets S1 and S2.

    In order to further verify the effectiveness of the model,an additional dataset named D4(which contains 8603 samples)is used in the experiments. The dataset D4 selects monthly data from the spring, summer, autumn, and winter for testing; these data are named T1(1000th-1999th samples),T2(3100th-4099th samples),T3 (5000th-5999th samples), and T4 (6000th-6999th samples).They are shown in Fig. 3. Table 2 shows the descriptive statistics of the related PM2.5concentrations.

    3.3. Results and discussion

    3.3.1. Results of RSAR

    The RSAR and KC are used to preprocess the original data. The attribute decision tables of each dataset are established according to the international PM2.5classification system. According to the international PM2.5concentration index classification standard,PM2.5concentration data are classified and discretized. Like the method of classifying PM2.5concentration levels,the concentrations of the other five air pollutants are discretized according to the levels.Table 3 shows the attribute reduction table for this study.By calculating the positive region values of the other five kinds of air pollutant concentrations and PM2.5concentrations, it can be determined that the significance degrees of PM10,NO2,CO,O3,and SO2are 0.0825, 0.0948, 0.0531, 0.2189, and 0.1843, respectively.O3and SO2have great significance and are judged to be the core attributes of the established information decision system.

    It should be noted that if the correlation between the reduction attribute and the decision attribute is too strong, there is no distinction between the two.If the correlation between the reduction attributes and decision attributes is too weak, there is no correlation between them. The reduction attributes in both cases are redundant.Therefore,in order to ensure the diversity of input samples, the selection of reduction attributes needs to comprehensively consider the correlation and independence between the reduction attributes and decision attributes. For this reason, this paper uses covariance to evaluate the relationship between PM2.5concentrations and other pollutant concentrations, as shown in Table 4. The cov(PM2.5, PM10), cov(PM2.5, NO2), cov(PM2.5, CO),and cov(PM2.5, SO2) are positive. The cov(PM2.5, O3) is negative.However, the absolute value of cov(PM2.5, PM10), cov(PM2.5, NO2),and cov(PM2.5, CO) is much larger than that of cov(PM2.5, SO2)and cov(PM2.5,O3).In terms of ensuring the independence of input attributes, the result of the RSAR algorithm can be verified to be effective. In order to avoid difficulty in model training caused by dimensional disaster, these more relevant attributes are selected as the core attributes, and other data with weak correlation become the reduction attributes.

    Fig. 2. PM2.5 concentrations series of S1 and S2.

    Fig. 3. PM2.5 concentrations series of T1-T4.

    Table 2 Descriptive statistics of PM2.5 concentrations.

    Table 3 Attribute reduction table.

    Table 4 Covariance table.

    3.3.2. Results of KC

    After the attribute reduction, the original dataset becomes an N × 3 sample space; three-dimensional (3D) KC is used to divide it into several similar clusters.The sum of the squared errors(SSE)[42]and the silhouette coefficient(SC)[43]are used to choose the best value of k.Because the clustering results of the three datasets are very similar, D1 is used as an example to show the results.

    Fig.4 shows different SSE and SC when choosing different k.The k value ranges from 1 to 15, and the SSE value decreases with the increase of the k value.When k=3,the SC value is the largest,but the SSE value is larger at the same time. Considering SSE and SC together in Fig. 4, the final k value is 7. Finally, the data of station D1 are divided into seven clusters.

    When k=7,the original data D1 are divided into seven groups;the results are shown in Fig. 5. In this figure,(a) shows the results for PM2.5,while the results for SO2and O3are presented separately in(b)and(c).The results of the KC for PM2.5shown in Fig.5(a)are the key part of this paper.Intuitively,the amplitude of cluster C1 is between 0 and 200 μg·m-3,and it fluctuates gently.The amplitude of C2 is between 0 and 55 μg·m-3, and fluctuates violently. However, the short period of C2 is obvious. The amplitude of C3 is between 0 and 400 μg·m-3. The fluctuation of C3 is smooth but the periodicity is not obvious. The amplitude of C4 is between 50 and 150 μg·m-3, and its periodicity and symmetry are good. The amplitude of C5 is between 0 and 200 μg·m-3, but it fluctuates more violently than that of C1. The amplitude of C6 is between 160 and 240 μg·m-3, fluctuates violently, and has strong symmetry.The amplitude of C7 is between 0 and 100 μg·m-3,and the period is obvious but the symmetry is weak. Overall, compared with the original data in Fig. 1, each kind of data becomes more stable after clustering. The peaks and troughs of each type of data show different intervals.

    The above description is only a subjective analysis; in order to draw a more convincing conclusion, descriptive statistics are used to further analyze the clustering results of PM2.5concentration data.Table 5 shows the descriptive statistics of D1 for the different clusters.

    The mean values of the seven groups of data are 71.54, 24.00,285.74,91.47,83.90,177.00,and 34.85 μg·m-3,respectively.Combined with the maximum and minimum values of each group of data, the seven groups of data after clustering are concentrated according to the value size,thereby reducing the fluctuation range of the data within the group.This is consistent with the amplitude distribution of each group of data in Fig. 5.

    The standard deviation (SD) reflects the dispersion degree among individuals in a group. The standard deviation values of the seven groups of data after clustering are 37.02, 14.25, 47.30,20.96, 32.70, 29.81, and 19.42 μg·m-3, respectively, which are all smaller than the 71.00 μg·m-3before clustering. The data of each group after clustering are closer to their average values.This trend is reflected in Fig.5: The symmetry of the upper and lower fluctuations of the data curves of each group is stronger.

    Fig. 4. SSE and SC with different k values.

    The skewness values of the seven groups of data after clustering are 0.70, 0.72, 0.74, 0.21, 1.01, 0.22, and 0.88, respectively, which are all smaller than the 2.01 before clustering.The wave peak symmetry of the data after clustering is stronger;that is,the cycle rule is more obvious. The kurtosis values of the seven groups of data after clustering are 3.23, 2.45, 2.50, 1.98, 4.00, 1.82, and 3.12,respectively, which are all smaller than the 8.64 before clustering.The extreme distribution of data in each group of data after clustering is reduced. In Fig. 5, the fluctuation of each group of data is smooth, and there is no obvious outlier. In other words, Fig. 5 and Table 5 show that the clustered data have the above advantages.

    The MCD-ESN model is used to analyze the series length in each cluster. In order to ensure the validity of the error evaluation, the first 80% of the data in each cluster is selected for model training and the last 20%is used for model prediction performance analysis.Table 6 shows the error evaluation of each cluster.

    Fig.5. (a) Results of KC for PM2.5;(b) results of KC for PM2.5 and SO2; (c) results of KC for PM2.5 and O3.

    Table 5 Descriptive statistics of D1 for different clusters.

    Table 6 Error evaluation of each cluster for D1 of MCD-ESN.

    When the sample size is greater than 1000,the amount of data has little effect on the prediction,as in C1,C3,C5,C6,and C7.However,when the number of samples is less than 1000,the prediction effect of the model is greatly reduced; this shows that the prediction effect of the ESN network is more sensitive to the number of low samples,as in C2 and C4.When the number of samples is small after clustering,the problem can be solved by increasing the number of samples in the original sequence.This will be a problem for future research.

    3.3.3. Forecasting accuracy and analysis

    In this study, six other prediction models are provided as comparison models to investigate the prediction performance of the proposed model. In addition, to investigate the multi-step prediction performance of the proposed model, all the involved models are conducted for the step-1 to step-3 predictions. The proposed model must forget a certain amount of output results due to the characteristics of the ESN algorithm[38].Therefore,the prediction accuracy fluctuates within a certain range.In this paper,this problem is solved by averaging the results of three repeated experiments. This solution does not increase the computing time cost by too much.

    The mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), standard deviation of error (SDE), Pearson’s correlation coefficient (R), and index of agreement (IA) are utilized to analyze the experimental results of the prediction models; the index values of the above models for D1, D2, and D3 are given in Table 7. As can be seen from Table 7,the three datasets reflect the same model performance. In order to keep the length of the paper within a reasonable range, only D1 is selected for specific analysis. The PM2.5concentration forecasting results for D1 are shown in Fig. 6. The R and IA results of the six prediction models for S1,S2,and T1-T4 are given in Table 8.The MAPE,MAE, RMSE, and SDE results of the six prediction models for S1 and S2 are given in Fig.7.The MAPE,MAE,RMSE,and SDE results of the six prediction models for T1 and T2 are given in Fig.8.The MAPE,MAE,RMSE and SDE results of the six prediction models for T3 and T4 are given in Fig. 9. It should be noted that since the values of R and IA do not belong to the same dimension as the other four evaluation indicators, they are not shown in the form of a graph.

    In Tables 7 and 8 and Figs. 6-9, the proposed model has the smallest error evaluation values, and thus achieves accurate prediction in PM2.5concentration forecasting. Compared with the other six comparison prediction models, the proposed model has better prediction accuracy from step-1 to step-3 predictions. This shows that the methods used in the hybrid model interact positively.

    The prediction accuracy of the ESN-PSO model is better than that of the ESN model.This phenomenon indicates that the optimal parameters selected by the PSO algorithm can help to improve the prediction accuracy of the ESN model. The prediction accuracy ofthe EWT-ESN-PSO model is better than that of the ESN-PSO model, which shows that the model accuracy can be improved by adding the EWT decomposition algorithm. The sequence obtained by the EWT algorithm is more stable and less random.Therefore, it is better to take the decomposed sublayers as the model input to obtain the prediction results. The prediction accuracy of the RSAR-KC-ESN model is better than that of the ESN model, which shows that the model accuracy can be improved by adding the RSAR-KC algorithm.After clustering,there is a larger difference between different groups of data and higher similarity between the same groups of data, which can improve the prediction accuracy of the original model to a certain extent.

    Table 7 Error evaluation for the PM2.5 concentrations of D1, D2, and D3.

    Moreover, the accuracy of each prediction model decreases as the number of steps increases in Table 7 and Figs. 6-9. With the increase of the forecasting step, the error accumulation will become increasingly serious, resulting in decline of the prediction accuracy.

    The city with the best air quality is Changsha(D3),followed by Guangzhou (D2). The air quality of Beijing (D1) is relatively poor.The forecasting accuracy in Table 7 and Fig. 6 is consistent with this order.Moreover,the data in Fig.7 show that samples with different pollution levels in the same area have no effect on model accuracy. The PM2.5concentrations of S1 are smaller than those of S2, but the prediction accuracy of S2 is higher than that of S1.Therefore, it can be concluded that the prediction accuracy of the proposed model is better in cities with better air quality than in cities with heavy pollution.

    In the above analysis,Tables 7 and 8 and Figs.6 and 7 verify the validity of the data forecasts for different cities over the same time period.In order to verify the validity of the prediction for the samecity over different time periods, the experiments shown in Figs. 8 and 9 were carried out. According to the data in Figs. 8 and 9,the proposed model maintained a stable prediction effect with the change of time period. In other words, the data in Figs. 8 and 9 verify the stability and validity of the proposed model over the whole year.

    Table 8 R and IA for the PM2.5 concentrations of S1, S2, and T1-T4.

    Fig. 6. Results of the various ahead-step predictions for the PM2.5 concentrations of D1. (a) Step-1; (b) step-2; (c) step-3.

    Fig. 7. Error evaluation for the PM2.5 concentrations of (a) S1 and (b) S2.

    Fig. 8. Error evaluation for the PM2.5 concentrations of (a) T1 and (b) T2.

    Fig. 9. Error evaluation for the PM2.5 concentrations of (a) T3 and (b) T4.

    In this study, the following computing is implemented in the simulation environments: Intel i5-6500 CPU 3.2 GHz, RAM 8 GB.Table 9 gives the computation time of the comparison models in D1. Because both RSAR-KC and PSO are offline processing, the computation time is not compare with them.

    The computation speed of the ESN is much faster than that of the LSTM, owing to the advantages of the ESN network itself.Because of the existence of the reserve pool, it is only necessary to train the output weight in the training process of the ESN network, which greatly improves the computing speed.

    After adding the EWT decomposition algorithm, the computational speed of the model decreases to a certain extent. Because each decomposition layer needs to be trained and predicted, the computational speed of the original model plays a vital role here,which further reflects the superiority of the ESN.

    The change in prediction steps has little effect on the calculation speed of the model. This may be because the computational capacity of the algorithm model is relatively large.

    4. Conclusions

    This study establishes an improved hybrid ESN forecasting model to predict and analyze the hourly average concentrationsof PM2.5based on the MCD method and the PSO. The proposed hybrid model is compared with several benchmark models to prove its effectiveness. The attribute reduction results show that the concentrations of SO2and O3play an important role in predicting the concentrations of PM2.5. Moreover, research on the influence of relevant meteorological parameters will be carried out in future studies. The clustering results show that PM2.5concentration data become stationary and regular after the clustering processing. These features are useful and conducive for ESN-based deep training. The prediction results show that: ① The MCD method can improve the accuracy of models; ②the proposed hybrid model has better prediction accuracy than other relevant deep learning or single models; ③the proposed hybrid model has achieved good experimental results with PM2.5pollutant concentration data from four cities in China; and ④the proposed hybrid PM2.5forecasting framework can also be applied in other air pollution time series multi-step predictions. The forecasted results can be embedded in relevant early warning systems for urban air pollution management.

    Table 9 Computation time of the comparison models in D1.

    The main contributions of this study can be summarized as follows:

    (1)A novel PM2.5concentrations multi-step prediction model is developed based on the MCD,ESN,and PSO,which yields accurate forecasting performance for hourly average PM2.5concentrations.The multi-step forecasting results can be used for the development of PM2.5pollution warning systems.

    (2)A novel decomposition method in hybrid PM2.5concentration forecasting named MCD is developed. This method integrates the feature extraction into decomposition.Multi-dimensional KC clustering is carried out using the feature extraction results of the RSAR algorithm,which not only guarantees the effectiveness of the clustering results, but also considers the influence of multidimensional features. The EWT algorithm-based KC algorithm is then employed for data preprocessing. The clustering algorithm is used to group the original PM2.5concentrations according to different PM2.5concentration scenarios. Next, combined with the EWT decomposition algorithm,raw PM2.5concentrations data are distinguished according to different characteristics in the timescale.Finally,the optimization function of the decomposition is realized.

    (3) In the proposed hybrid PM2.5concentration forecasting model, the ESN is employed as a predictor. The sparse connection of neurons in the reservoir of the ESN not only improves the convergence of the neural network model, but also enhances the model generalization. This characteristic can reduce the probability of the overfitting problem in the process of model training.Moreover, the ESN has good real-time performance in computing.

    Acknowledgements

    The study is fully supported by the National Natural Science Foundation of China (61873283), the Changsha Science &Technology Project (KQ1707017) and the Innovation Driven Project of the Central South University (2019CX005).

    Compliance with ethics guidelines

    Hui Liu, Zhihao Long, Zhu Duan, and Huipeng Shi declare that they have no conflict of interest or financial conflicts to disclose.

    日本免费一区二区三区高清不卡| 欧美色欧美亚洲另类二区| 欧美中文综合在线视频| 国产伦精品一区二区三区四那| 成人国产综合亚洲| 亚洲精品日韩av片在线观看 | 91麻豆av在线| 久久久久免费精品人妻一区二区| 久久久久久九九精品二区国产| avwww免费| 国产成人福利小说| 日本一二三区视频观看| 高清在线国产一区| 国产精品99久久99久久久不卡| 99国产极品粉嫩在线观看| 一二三四社区在线视频社区8| 天美传媒精品一区二区| 男人和女人高潮做爰伦理| 成人亚洲精品av一区二区| 久久精品国产综合久久久| 国产99白浆流出| 啦啦啦免费观看视频1| 久久久久久久午夜电影| 国产欧美日韩一区二区精品| 久久久久久久亚洲中文字幕 | 午夜福利欧美成人| 亚洲av电影在线进入| 国产精品久久视频播放| 色尼玛亚洲综合影院| 欧美+日韩+精品| 日本免费一区二区三区高清不卡| 国产av不卡久久| 亚洲精品久久国产高清桃花| 国产高清videossex| 熟妇人妻久久中文字幕3abv| 亚洲自拍偷在线| 国产三级黄色录像| 一进一出抽搐动态| 午夜精品一区二区三区免费看| 国产精品99久久99久久久不卡| 国产真实伦视频高清在线观看 | 国产蜜桃级精品一区二区三区| 免费看美女性在线毛片视频| 欧美3d第一页| 国产精品亚洲美女久久久| 国内少妇人妻偷人精品xxx网站| 宅男免费午夜| 十八禁人妻一区二区| 亚洲av电影不卡..在线观看| 老熟妇乱子伦视频在线观看| 久久精品夜夜夜夜夜久久蜜豆| 51午夜福利影视在线观看| 在线免费观看不下载黄p国产 | 亚洲av电影不卡..在线观看| 国产精品98久久久久久宅男小说| 丰满的人妻完整版| 在线观看午夜福利视频| 国产 一区 欧美 日韩| 手机成人av网站| 色在线成人网| 中文字幕熟女人妻在线| 国产97色在线日韩免费| 人人妻人人看人人澡| 两个人的视频大全免费| 麻豆国产97在线/欧美| 嫁个100分男人电影在线观看| 亚洲久久久久久中文字幕| 国产精品99久久久久久久久| 亚洲精品成人久久久久久| 我要搜黄色片| 欧美日韩亚洲国产一区二区在线观看| 99久久九九国产精品国产免费| 日本与韩国留学比较| 久久精品综合一区二区三区| 欧美丝袜亚洲另类 | 国产高清videossex| 国语自产精品视频在线第100页| 亚洲乱码一区二区免费版| 午夜福利免费观看在线| 亚洲人成电影免费在线| 国产亚洲精品一区二区www| 成人国产一区最新在线观看| 欧美性猛交╳xxx乱大交人| 一进一出抽搐动态| 特级一级黄色大片| 国产精品香港三级国产av潘金莲| 午夜免费激情av| 国产aⅴ精品一区二区三区波| 婷婷精品国产亚洲av| 色噜噜av男人的天堂激情| 搡老妇女老女人老熟妇| 99久久综合精品五月天人人| 亚洲精品一卡2卡三卡4卡5卡| 五月伊人婷婷丁香| 精品久久久久久久末码| 久久久久久久亚洲中文字幕 | 在线免费观看的www视频| 欧美xxxx黑人xx丫x性爽| 亚洲avbb在线观看| 中文亚洲av片在线观看爽| 国产蜜桃级精品一区二区三区| 中亚洲国语对白在线视频| 欧美极品一区二区三区四区| 亚洲人成网站在线播放欧美日韩| 天堂√8在线中文| 久久久色成人| 高潮久久久久久久久久久不卡| 久久精品91蜜桃| 亚洲精品色激情综合| 日本一二三区视频观看| 中文字幕av在线有码专区| 日韩人妻高清精品专区| 超碰av人人做人人爽久久 | 三级国产精品欧美在线观看| 床上黄色一级片| 成人av在线播放网站| 成人欧美大片| 国产成人系列免费观看| 午夜老司机福利剧场| 婷婷精品国产亚洲av| 九九久久精品国产亚洲av麻豆| 91久久精品国产一区二区成人 | 国产精品久久久久久亚洲av鲁大| 亚洲av熟女| 亚洲av不卡在线观看| 欧美激情久久久久久爽电影| 高清日韩中文字幕在线| 禁无遮挡网站| 国产亚洲精品综合一区在线观看| av欧美777| 啪啪无遮挡十八禁网站| 国产精品久久电影中文字幕| 老汉色∧v一级毛片| 国产一区二区激情短视频| 国产中年淑女户外野战色| 好看av亚洲va欧美ⅴa在| 亚洲国产精品999在线| 51午夜福利影视在线观看| 精品久久久久久成人av| 日韩欧美 国产精品| 淫秽高清视频在线观看| 成年免费大片在线观看| 国语自产精品视频在线第100页| 国产精品,欧美在线| 亚洲五月婷婷丁香| 9191精品国产免费久久| 国产高清激情床上av| 免费看十八禁软件| 真人做人爱边吃奶动态| 五月伊人婷婷丁香| 久久久色成人| 一个人看视频在线观看www免费 | 天堂av国产一区二区熟女人妻| 中文字幕av成人在线电影| 国产一区二区三区在线臀色熟女| 亚洲avbb在线观看| 老熟妇仑乱视频hdxx| 噜噜噜噜噜久久久久久91| 久久精品国产99精品国产亚洲性色| 成年免费大片在线观看| 美女被艹到高潮喷水动态| 日本免费一区二区三区高清不卡| 91久久精品国产一区二区成人 | 亚洲国产精品sss在线观看| 国产精品久久久久久人妻精品电影| 国产精品 欧美亚洲| 国产亚洲精品久久久久久毛片| 男女床上黄色一级片免费看| www国产在线视频色| 老熟妇乱子伦视频在线观看| 久久香蕉国产精品| 性欧美人与动物交配| 老熟妇乱子伦视频在线观看| 亚洲av美国av| 欧美丝袜亚洲另类 | 亚洲av中文字字幕乱码综合| avwww免费| 国产亚洲av嫩草精品影院| 日日摸夜夜添夜夜添小说| 精品人妻1区二区| 狠狠狠狠99中文字幕| 国产精品永久免费网站| 大型黄色视频在线免费观看| 免费搜索国产男女视频| 怎么达到女性高潮| 少妇丰满av| 国产成人影院久久av| 国产欧美日韩精品一区二区| 欧美bdsm另类| 在线观看免费午夜福利视频| 婷婷精品国产亚洲av| 国产欧美日韩精品亚洲av| x7x7x7水蜜桃| 国产老妇女一区| 有码 亚洲区| 最新在线观看一区二区三区| 日本黄色视频三级网站网址| 超碰av人人做人人爽久久 | 在线视频色国产色| 一本一本综合久久| 99久久九九国产精品国产免费| 88av欧美| 国产v大片淫在线免费观看| 在线天堂最新版资源| 90打野战视频偷拍视频| 国产av一区在线观看免费| 久久精品91蜜桃| 亚洲性夜色夜夜综合| 亚洲人成网站在线播| 我的老师免费观看完整版| 香蕉丝袜av| 丰满人妻熟妇乱又伦精品不卡| 色哟哟哟哟哟哟| 99国产综合亚洲精品| 国产精品久久电影中文字幕| 国产av在哪里看| 亚洲黑人精品在线| 在线观看舔阴道视频| 男女做爰动态图高潮gif福利片| 欧美乱妇无乱码| 一本综合久久免费| 亚洲熟妇中文字幕五十中出| 欧洲精品卡2卡3卡4卡5卡区| 亚洲精品在线观看二区| 午夜免费激情av| 搡老熟女国产l中国老女人| www.色视频.com| 国产欧美日韩一区二区精品| 波野结衣二区三区在线 | 亚洲第一电影网av| 亚洲国产精品999在线| 久久性视频一级片| 757午夜福利合集在线观看| 欧美黑人欧美精品刺激| 欧美成人免费av一区二区三区| 精品一区二区三区人妻视频| 91久久精品国产一区二区成人 | 香蕉av资源在线| bbb黄色大片| 国产乱人伦免费视频| 久久久久久久午夜电影| 男女那种视频在线观看| avwww免费| 国产成人aa在线观看| 亚洲国产精品合色在线| 乱人视频在线观看| 99热精品在线国产| 91麻豆精品激情在线观看国产| 黄色片一级片一级黄色片| h日本视频在线播放| 19禁男女啪啪无遮挡网站| 亚洲avbb在线观看| 欧美日本亚洲视频在线播放| 99在线视频只有这里精品首页| 内射极品少妇av片p| 亚洲熟妇中文字幕五十中出| 国产精品 欧美亚洲| 欧美不卡视频在线免费观看| 国产精品一区二区三区四区久久| 亚洲avbb在线观看| 欧美日本亚洲视频在线播放| 日韩有码中文字幕| 99久久精品一区二区三区| 国产精品久久久久久亚洲av鲁大| 桃红色精品国产亚洲av| 夜夜躁狠狠躁天天躁| 免费在线观看影片大全网站| 亚洲精华国产精华精| 国产亚洲精品综合一区在线观看| АⅤ资源中文在线天堂| 高清在线国产一区| 免费看a级黄色片| 久久九九热精品免费| 伊人久久精品亚洲午夜| 99在线人妻在线中文字幕| 亚洲久久久久久中文字幕| 亚洲精品亚洲一区二区| 麻豆国产av国片精品| 757午夜福利合集在线观看| 亚洲精品国产精品久久久不卡| 法律面前人人平等表现在哪些方面| 午夜福利高清视频| 午夜精品在线福利| 国产精品1区2区在线观看.| 国产精品自产拍在线观看55亚洲| 成人欧美大片| 国产精品久久久久久精品电影| 热99re8久久精品国产| 中文亚洲av片在线观看爽| 国产黄色小视频在线观看| 久久久精品大字幕| 国产乱人视频| 欧美成狂野欧美在线观看| 免费看光身美女| 老汉色av国产亚洲站长工具| 亚洲人成网站在线播| 神马国产精品三级电影在线观看| 一级作爱视频免费观看| 精品一区二区三区视频在线观看免费| 亚洲精品影视一区二区三区av| 欧美高清成人免费视频www| 精品乱码久久久久久99久播| 精品一区二区三区视频在线 | 久久午夜亚洲精品久久| 小说图片视频综合网站| 99久久无色码亚洲精品果冻| 又黄又粗又硬又大视频| 综合色av麻豆| 波多野结衣巨乳人妻| 国产伦精品一区二区三区视频9 | 精品人妻偷拍中文字幕| 亚洲五月婷婷丁香| 欧美区成人在线视频| 成人午夜高清在线视频| 欧美绝顶高潮抽搐喷水| 香蕉丝袜av| 天堂av国产一区二区熟女人妻| 91麻豆精品激情在线观看国产| 婷婷丁香在线五月| 每晚都被弄得嗷嗷叫到高潮| 18禁裸乳无遮挡免费网站照片| 亚洲欧美日韩东京热| 国产一区二区三区在线臀色熟女| 成人精品一区二区免费| 国产三级黄色录像| 成人一区二区视频在线观看| 免费高清视频大片| 观看免费一级毛片| 免费电影在线观看免费观看| svipshipincom国产片| 亚洲精品日韩av片在线观看 | 日韩中文字幕欧美一区二区| 天天躁日日操中文字幕| 岛国在线观看网站| 国产精品女同一区二区软件 | svipshipincom国产片| 在线视频色国产色| 久久久国产成人精品二区| 精品电影一区二区在线| 麻豆成人午夜福利视频| 日韩中文字幕欧美一区二区| 高清毛片免费观看视频网站| 国产亚洲精品久久久久久毛片| 啪啪无遮挡十八禁网站| 亚洲色图av天堂| 免费看美女性在线毛片视频| 香蕉久久夜色| 在线观看美女被高潮喷水网站 | netflix在线观看网站| 19禁男女啪啪无遮挡网站| 国产高清三级在线| 宅男免费午夜| 在线播放国产精品三级| 草草在线视频免费看| www国产在线视频色| 国产男靠女视频免费网站| 国产淫片久久久久久久久 | 悠悠久久av| 99riav亚洲国产免费| 日韩精品中文字幕看吧| 国内毛片毛片毛片毛片毛片| 在线观看舔阴道视频| 国产一区二区在线av高清观看| 蜜桃久久精品国产亚洲av| 国产精品久久久久久亚洲av鲁大| 精品欧美国产一区二区三| 熟女电影av网| 国产亚洲精品久久久com| 精品不卡国产一区二区三区| 一区二区三区免费毛片| 国产视频一区二区在线看| 丝袜美腿在线中文| 国产精品一区二区三区四区免费观看 | 日本撒尿小便嘘嘘汇集6| 日韩人妻高清精品专区| 三级毛片av免费| 免费在线观看影片大全网站| 亚洲人成电影免费在线| 好男人电影高清在线观看| 亚洲欧美日韩高清专用| 一进一出抽搐gif免费好疼| 给我免费播放毛片高清在线观看| 动漫黄色视频在线观看| 亚洲欧美日韩高清专用| 老熟妇仑乱视频hdxx| 99在线视频只有这里精品首页| 亚洲中文字幕一区二区三区有码在线看| a在线观看视频网站| 日本成人三级电影网站| 日本一二三区视频观看| 身体一侧抽搐| 国产精品美女特级片免费视频播放器| 亚洲精品亚洲一区二区| 97超级碰碰碰精品色视频在线观看| 日本免费一区二区三区高清不卡| 在线免费观看不下载黄p国产 | 在线a可以看的网站| 亚洲黑人精品在线| 在线观看66精品国产| 精品久久久久久久末码| 国产精品久久久久久久电影 | 国产爱豆传媒在线观看| 亚洲成人久久爱视频| 国产高清视频在线播放一区| 三级毛片av免费| 此物有八面人人有两片| 亚洲人与动物交配视频| 免费看a级黄色片| 国产69精品久久久久777片| 亚洲真实伦在线观看| 成年免费大片在线观看| 五月伊人婷婷丁香| 久久伊人香网站| www.色视频.com| 欧美大码av| 18禁国产床啪视频网站| 露出奶头的视频| 日韩欧美在线乱码| 国产伦人伦偷精品视频| 欧美精品啪啪一区二区三区| 91在线精品国自产拍蜜月 | 成年人黄色毛片网站| 亚洲电影在线观看av| 欧美乱妇无乱码| 老汉色∧v一级毛片| 在线观看日韩欧美| 男人舔奶头视频| 午夜激情欧美在线| 免费看a级黄色片| 欧美中文日本在线观看视频| avwww免费| 99久久成人亚洲精品观看| 色吧在线观看| 最近最新中文字幕大全电影3| 宅男免费午夜| 啦啦啦韩国在线观看视频| 天堂√8在线中文| 亚洲av中文字字幕乱码综合| 叶爱在线成人免费视频播放| 成人国产综合亚洲| a在线观看视频网站| 午夜福利在线观看免费完整高清在 | 亚洲精品久久国产高清桃花| 亚洲av二区三区四区| 在线观看午夜福利视频| 悠悠久久av| 午夜激情欧美在线| 丁香欧美五月| av黄色大香蕉| 亚洲精品粉嫩美女一区| 搡老熟女国产l中国老女人| 精品免费久久久久久久清纯| av福利片在线观看| 毛片女人毛片| 日本免费一区二区三区高清不卡| 美女高潮的动态| 日韩 欧美 亚洲 中文字幕| 99热这里只有精品一区| 日韩欧美三级三区| 小蜜桃在线观看免费完整版高清| 国内毛片毛片毛片毛片毛片| 国产黄片美女视频| 国产精品嫩草影院av在线观看 | 怎么达到女性高潮| 久久久精品欧美日韩精品| 欧美绝顶高潮抽搐喷水| 免费看十八禁软件| 欧美一区二区精品小视频在线| aaaaa片日本免费| www日本黄色视频网| 99久久成人亚洲精品观看| 亚洲国产欧洲综合997久久,| 波多野结衣巨乳人妻| 观看美女的网站| 婷婷精品国产亚洲av| 给我免费播放毛片高清在线观看| 久久精品人妻少妇| 亚洲欧美精品综合久久99| 免费av毛片视频| 看免费av毛片| 舔av片在线| 免费看光身美女| 国模一区二区三区四区视频| 91久久精品电影网| 又爽又黄无遮挡网站| 少妇丰满av| 国产高清三级在线| 一个人免费在线观看的高清视频| 老司机深夜福利视频在线观看| 久久天躁狠狠躁夜夜2o2o| 精品国产亚洲在线| x7x7x7水蜜桃| 久久精品夜夜夜夜夜久久蜜豆| 国产精品一区二区免费欧美| 最近视频中文字幕2019在线8| 久久精品国产亚洲av涩爱 | 欧美日韩亚洲国产一区二区在线观看| 99热只有精品国产| 国产亚洲精品av在线| 69av精品久久久久久| 欧美三级亚洲精品| 在线看三级毛片| 国内揄拍国产精品人妻在线| 亚洲欧美日韩高清在线视频| 叶爱在线成人免费视频播放| 在线a可以看的网站| 精品熟女少妇八av免费久了| 97人妻精品一区二区三区麻豆| 国产久久久一区二区三区| 97超视频在线观看视频| 中文字幕av在线有码专区| av视频在线观看入口| 在线观看免费视频日本深夜| 欧美日韩福利视频一区二区| 精品久久久久久,| 国产在视频线在精品| 久久久久久国产a免费观看| 亚洲欧美日韩东京热| 桃红色精品国产亚洲av| 国产av在哪里看| 日本在线视频免费播放| 国产精品日韩av在线免费观看| 伊人久久精品亚洲午夜| 给我免费播放毛片高清在线观看| 国产三级在线视频| 一本精品99久久精品77| 久久久精品欧美日韩精品| 婷婷精品国产亚洲av| 欧美日韩一级在线毛片| 国产一区在线观看成人免费| 男女那种视频在线观看| 欧美性感艳星| 久久国产精品影院| 一级黄片播放器| 草草在线视频免费看| 免费人成在线观看视频色| 亚洲av第一区精品v没综合| 亚洲 欧美 日韩 在线 免费| 夜夜夜夜夜久久久久| 一进一出好大好爽视频| 欧美高清成人免费视频www| 欧美成人a在线观看| 国内少妇人妻偷人精品xxx网站| 色综合欧美亚洲国产小说| 国产高清视频在线观看网站| 欧美绝顶高潮抽搐喷水| 成年人黄色毛片网站| 精品久久久久久,| 国产在线精品亚洲第一网站| 亚洲av一区综合| 动漫黄色视频在线观看| 又爽又黄无遮挡网站| 精品乱码久久久久久99久播| 欧美日韩国产亚洲二区| 午夜福利视频1000在线观看| 国产午夜精品久久久久久一区二区三区 | 欧美日韩国产亚洲二区| 午夜免费激情av| 午夜影院日韩av| 日本一本二区三区精品| 观看美女的网站| 999久久久精品免费观看国产| 国产成人欧美在线观看| 日韩欧美三级三区| 特级一级黄色大片| 亚洲av免费高清在线观看| 91av网一区二区| 99热这里只有是精品50| 亚洲av第一区精品v没综合| av在线蜜桃| 国产精品久久久久久久久免 | 乱人视频在线观看| 在线天堂最新版资源| 亚洲一区高清亚洲精品| 黄片小视频在线播放| 亚洲国产日韩欧美精品在线观看 | 亚洲 国产 在线| www.熟女人妻精品国产| 亚洲av电影在线进入| 国产精品亚洲一级av第二区| 97超级碰碰碰精品色视频在线观看| 国产精品,欧美在线| 无限看片的www在线观看| 欧美乱妇无乱码| 国产一区在线观看成人免费| 99久久精品一区二区三区| 嫩草影院入口| 女人高潮潮喷娇喘18禁视频| 国产精品一区二区免费欧美| 亚洲七黄色美女视频| 久久久久久九九精品二区国产| 亚洲精品乱码久久久v下载方式 | 欧美日韩瑟瑟在线播放| 欧美日韩福利视频一区二区| 久久婷婷人人爽人人干人人爱| 波多野结衣巨乳人妻| 性色avwww在线观看| 日韩成人在线观看一区二区三区| 亚洲欧美日韩高清在线视频| 91久久精品国产一区二区成人 | 国产精华一区二区三区| 精华霜和精华液先用哪个| 国产伦精品一区二区三区四那| 一区二区三区免费毛片| 国产午夜精品久久久久久一区二区三区 | 国产精品国产高清国产av| 国产真实乱freesex| 99久久精品热视频| 欧美日韩国产亚洲二区| 久9热在线精品视频| 热99在线观看视频| 欧美日本视频| 欧美色视频一区免费| 男女做爰动态图高潮gif福利片| 亚洲国产日韩欧美精品在线观看 |