A IR N ET : A MACHINE LEARNING DATASET FOR AIR QUALITY FORECASTING
Anonymous authors
Paper under double-blind review
A BSTRACT
In the past decade, many urban areas in China have suffered from serious air pollution problems, making air quality forecast techniques a hot spot.
Conventional approaches rely on numerical methods to estimate the pollutant concentration and require lots of computing power.
To solve this problem, we applied deep learning methods which have already achieved major breakthroughs in many other areas.
Deep learning requires large-scale datasets to train an effective model.
In this paper, we introduced a new dataset, entitled as AirNet 1 , containing the 0.25 longitudinal and latitudinal degree grid map of mainland China, with more than two years of continued air quality measurement and meteorological data.
We published this dataset as an open resource for machine learning researches and set up a baseline to a 5-day air pollution forecast.
Through our experiments, it was demonstrated that this dataset could facilitate the development of new algorithms on forecasting the air quality.
I NTRODUCTION
In recent years, along with economic development, air pollution in developing countries such as
China and India has become a severe problem threatening the public health(Pun et al.
(2014), Lv et al.
The air quality forecasting techniques are being rapidly upgraded as the demand for measuring pollution increases.
On one side, some models like HYSPLIT-4 and KF, utilize atmospheric dynamic processes, which attempt to ﬁgure out the accumulation and dissipation mechanisms of air pollutants (Lv et al.
; Djalalova et al.
Some people also used the hidden Makov model like the
FL method, to predict pollutant concentrations (Sun et al.
; Yetilmezsoy & Abdul-Wahab (2012)).
However, due to the complexity of air transport dynamics, the conventional forecasting models demand a great deal of computing resources.
In addition, the model’s accuracy depends on the model structure itself and cannot improve regardless of the amount of training data it is provided.
On the other hand, the deep learning approach (LeCun et al.) has achieved exceptional results in unstructured information processing, such as computer vision, speech recognition, and natural language processing (Hinton et al.
; Zhang & LeCun (2015); Krizhevsky et al.
(2012)).In these tasks, the deep learning method has outperformed conventional machine learning methods.
Inspired by this, people attempted to apply deep learning models such as the Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), to perform meteorological forecasting Shangzan et al.
(2017), including estimating of precipitation probability and air pollution.
Notably, to apply deep learning techniques effectively, a good model requires a large-scale dataset (Liu et al.
However, to our knowledge, such a dataset is absent in the air quality forecast ﬁeld so far.
A good dataset could greatly incent the industry to develop the new models and offer a uniﬁed assessment standard, as in the case of ImageNet (Deng et al.
in the computer vision ﬁeld .
To facilitate research and data collection, we processed and published an air quality dataset, AirNet, so as to ﬁll this gap.
Furthermore, we conducted experiments to validate the capability of this dataset, and set up a baseline for air pollution prediction on the AirNet dataset.
[Table]
1. R ELATED W ORK
Shi et al.
(2015) proposed RNN and convolutional LSTM to forecast the precipitation in future two hours, which they formalized as a spatiotemporal issue.
The air quality forecast is similar to weather forecast, but two factors make air quality forecast more difﬁcult and distinct from estimating precipitation; 1) the time span of air quality forecast is longer than weather forecast, the former often forecasts in four or ﬁve days, sometimes even goes beyond ten days.
2) additional inﬂuential factors must be considered in air quality forecast, such as the dynamics of air pollutants and the interaction with meteorological conditions.
Modeling with AirNet dataset, the difference will be explicated in
Section 5 below.
Ong et al.
(2016) applied RNN to predict PM2.5 with environmental sensor data, which improved the results accuracy.
Kurt & Oktay (2010)s research on forecasting air pollution with neural networks demonstrated the methods superiority and feasibility .
Liang et al.
(2015) released a dataset containing the value of PM2.5 which is only measured in Beijing.
After this, Liang et al.
(2016) published a larger dataset to analyze the pollutant factor in ﬁve cities of China.
All datasets used above are point-wise data, which do not allow us to model in a spatially explicit manner.
In addition, as Table 2 shows, these datasets are too small to train a deep neural network.
Thus it became essential and urgent to set up a larger scale training dataset to enhance the accuracy of the forecast results.
1. C ONTRIBUTION AND R EVIEW
In this paper, we delivered a dataset, AirNet, containing more than two years of 7 indices of air quality data from 1498 stations, which is at least 40 times larger than most previous datasets.
We set up a baseline based the LSTM model with AirNet dataset.
The results demonstrated the effectiveness of the deep learning model for the air quality forecast.
D ATASET E XTRACTION 2. D ATASET COLLECTION
At ﬁrst, we collected the data from China National Environmental Monitoring Center (CNEMC) which runs the 1498 monitor stations spreading across the whole country.
Every station monitors air quality in real-time and reports the quality of air quality every hour.
Therefore we wrote a spider to fetch the data from the Data-Publish platform 2 .
Secondly, we gathered the meteorological data from the Global Forecast System (GFS) which contains the 7 meteorological condition features as in Table 1.
2. A LIGNMENT D ATA
For air quality data from CNEMC, in each city, there are several monitoring stations of the real-time air pollutant concentration per hour.
The GFS data format is a 3-dimensional matrix, released by the National Oceanic and Atmo- spheric Administration (NOAA) every six hours.
Every release contains the meteorological condition fea-
[Table]
tures forecasting for 10 days in every 3 hours, and 16 days in every 12 hours.
For each meteorological condition features like TEMP, there are 1038240 values at one time point globally.
We converted this data to a matrix which equals the volume of (180 * 4 + 1) * (360 * 4), where 180 is the radial latitude, 360 is the radial of longitude, and 4 is the invert of the 0.25 resolution.
Subsequently, we also obtained the global forecasting models geospatial information.
These two kinds of data sources are distinct.
GFS data is a 3 dimensional matrix while the air quality data is a two dimensional matrix at a time point, where one dim is station ID, and the other is air quality indices.
Therefore we had to align these two datasets.
For each GFS data time point, we selected required air quality data for all stations in China and interpolated them into a matrix covering the whole country.
Radial Basis Function is used to interpolate Air data to a matrix as shown in Figure 1.
After that, we concatenated the gfs dataset with air quality dataset as in Figure 2 and thus produced a fourdimensional dataset (latitude, longitude, timesteps, features).
Latitude ranges from 75 degrees to 132 degrees and the north latitude range of is from 18 degrees to 51 degrees.
The grid resolution is 0.25 degree and the data was collected from April 1, 2015, to September 1, 2017.
Every 3 hours there is one frame; on every frame we have 6 GFS features, and 7 types of the air quality indices.
In total, we had a matrix with the dimension of (132, 228, 7072, 13).
We took partial features of 2:00AM, January 23, 2017 as an example in Figure 2.
Some statistic features of the AirNet 3 are displayed in Table 2.
M ACHINE LEARNING TASK 3. T ASKS D ESCRIPTION
Air quality forecast is different to precipitation prediction because many factors will affect the concentration and distribution of the air pollutants.
It is also essential to take the future meteorological conditions into consideration rather than just the history of pollutant concentration.
Based on such principles, we formalized the pollutant prediction as follows:
[Equation]
In this formula, the air pollutant concentrations was taken into the time step t as x t .
We converted the problem of the pollutant prediction as time sequential prediction problems, as in the case of giving the past pollutant concentration x 0 to x t − 1 .
Since future meteorological conditions are as important as historical pollutant concentrations, the meteorological factors were taken as b 0 to b t − 1 plus the predicted future meteorological condition b t .
We could produce future meteorological predictions through numerical methods, and feed the predicted data into the model, as time t increments, above step is repeated, so we could forecast air pollutant concentration as far as possible.
In particular, our goal is to model and ﬁt the conditional probability of the AirNet dataset.
3. M ETRICS
In the development of machine learning models, we use Mean Square Error as a loss function.
Since the estimated pollutant value became more precise closer to the monitoring stations, we modiﬁed the MSE using a new calculation method, as the Point-wise Mean Square Error (PMSE), which only calculates loss in the nearby stations.
The experiments results demonstrate that this improvement is evident and beneﬁcial.
[Equation]
[Table] [Table]
Additionally, we used the Probability of Detection (POD), the False Alarm Rate (FAR), and the
Critical Success Index (CSI)Shi et al.
as the metrics for the assessment.
These metrics are intuitive for us to understand the performance of a system.
We set the PM25 value 80 (ug/m3), as a threshold value to divide the air quality results into two levels, i.e. days with a pollutant concentration higher than 80, as the pollution days and vice versa, as in Table 3.
Based on this deﬁnition, we designed the following formula:
[Equation]
hits
[Equation] [Equation]
[Equation]
hits
[Equation] [Equation]
B ASELINE
For the pollutant prediction described in 1, we developed a convolutional enhanced sequence model named WipeNet with two steps.
Firstly, to capture the air pollutant accumulation and dissipation relationships between sequential time steps, a reduced LSTM module was used to.
Secondly, to deal with the transfer effect between different places, a convolutional model with learned localvariant kernel was used to redistribute the pollutant concentrations between the data point and its neighborhood.
4. R EDUCED LSTM
Hochreiter & Schmidhuber (1997) proposed the Long Short-Term Memory (LSTM) model to alleviate the gradient vanishing problem, which was successfully applied in the ﬁeld of natural language processes.
In order to optimize the algorithms performance in the ﬁeld ofair quality forecasting, we modiﬁed the model of LSTM to ﬁt in the air pollutant change.
The modiﬁcation is demonstrated below.
The original LSTM model is:
[Equation]
We modiﬁed this formula as follow 4 :
[Equation]
The input and output gate were removed, as suggested in Jozefowicz et al.
In the above formulas, x t represents the meteorological conditions, and we regard i t , as the accumulation of air pollutants induced by adverse weather conditions like higher tempratur , no wind etc..
By adding this accumulation term into c t − 1 ,we tried to simulate the air pollutant concentration changing under static stability weather, while using the forget gate to simulate the removal factors like wind, rainfall, and high humidity.
After this process, we stored the air pollutant concentration for every time steps in c t and the subtracted the mean value which was calculated in advance to render h t as the ﬂuctuation value at every time step, then feeding h t and c t to the next time step.
After substituting LSTM with the
ReducedLSTM, we achieved a better result.
4. W IPE N ET
In the above LSTM and ReducedLSTM method, we predicted the future air pollutant concentration without considering the data from nearby places.
In the real physical environment, however, the ﬂow in atmosphere would deﬁnitely transport pollutant from one place to another.
The pollutant concentration could also change because of different meteorological conditions.
We considered this pollutant transport as a redistribution around a place regionally and used a local convolutional operation to encode such processes.
A softmax layer is also added after the convolutional operation to characterize the redistribution phenomenon .
To better featuring the different places, the covolutional knernel should be different for each regional wind ﬁelds, which was learnt from the local wind ﬁeld by another standard convolutional operation.
Our ﬁnal formula is deﬁned as:
[Equation]
In brief, we used (cid:98) c to denote the pollutant change caused by its own meteorological condition, and then used a learned convolutional kernel to redistribute the pollutant concentration within the neighborhood.
E XPRIMENTS
In the previous study, Liang et al. (2015) demonstrated that pollutant concentration is signiﬁcantly affected by meteorological conditions, hence we feed the meteorological condition at every time steps as the control information.
Preliminarily, we found the Reduced LSTM outperformed the
LSTM and Gated Recurrent Unit (GRU).
We chose PM2.5 as the forecasting object of the air pollutant, and selected October 5, 2016, to January 3, 2017 data, as the training dataset, and January 3, 2017, to February 2, 2017,data as the validation dataset.
As there is one data point every three hours, and we predict air pollutant concentration for 5 days, we sliced each dataset into 40 time steps length segment, allowing overlaying segments.
In total, 680(90 * 8 - 40) matrix samples in the training dataset, and 200(30 * 8 - 40) matrix samples in the validation dataset were obtained, respectively.
Because the methods of LSTM and ReducedLSTM did not use the spatial relationship, we shufﬂe our data on the locational dimension.
For better precision, we chose the datapoints logged at monitor station in the HuaBei area and got 91120(for every matrix
we got 134 stations) samples for training and 26800 samples for validation.
The test dataset was set from October 5, 2015, to January 3, 2016.
[Table]
We implemented all code through Keras(Chollet et al. (2015)), and chose TheanoBergstra et al.
(2011) as backend, using the built-in LSTM and GRU modules.
For simpliﬁcation, we trained and predicted using subtracted PM25 data between two consecutive time steps.
We preprocessed the meteorological condition information through two multiple perceptrons (MLP), as each dimension of MLP is 20.
We set the batchsize to 32 and the dropout rate to 0.2, we ran 30 epoch with the patience option of 10.
After every 10 experiments, the average value was calculated and the results are displayed in Table 4.
The results demonstrated that, without taking the meteorological condition into consideration, the accuracy of the prediction results dramatically deteriorated.
Furthermore, reduced LSTM is improved than LSTM, we assumed this is because our equation considered air pollutant dynamics, thus we gave more information to model than LSTM while keeping LSTMs advantage.
Finally, we implemented the WipeNet, with kernel size of (5, 5), and initialize weight with the
Glorot Uniform Distribution, the dataset and the other settings were left unchanged.
We could see that the WipeNet achieved best accuracy on all of our three criterions.
We attributed the progress to the integration of more information to the model, taking the transportation factor between different areas into consideration.
C ONCLUSION
In this paper, we publicized a new dataset, AirNet, for researchers who want to use deep learning method to analyze air quality.
Compared to previous studies in the ﬁeld, it contains 7 indices of air quality from 1498 monitoring stations, which is at least 40 times larger than most previous datasets.
In addition, we set up a baseline method WipeNet, for 5-day air quality prediction using AirNet dataset, and received a CSI score of 0.56, which achieved a 16% point improvement compared to classic LSTM methods.
6. F UTURE WORK
In the future we plan to add more data types into AirNet for example we only used ground meteorological data in this paper but data from multiple heights can reveal the change of inversion temperature layer which is a crucial factor for air quality forecast.
We wish AirNet would not only be applied to air quality forecast but also be utilized to reveal the critical factors in the causality of air pollution.
For example, if we combine land-cover data with air pollution change, we may ﬁnd some interactions between them.
Perhaps we could even ﬁnd new methods to reduce air pollution and give our children a brighter future.