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A publication of

CHEMICAL ENGINEERING TRANSACTIONS
VOL. 83, 2021

The Italian Associationof Chemical Engineering

Online at www.cetjournal.it

Guest Editors: Jeng Shiun Lim, Nor Alafiza Yunus, Jiří Jaromír Klemeš
Copyright © 2021, AIDIC Servizi S.r.l.
ISBN 978-88-95608-81-5; ISSN 2283-9216

DOI: 10.3303/CET2183060

Convolution Recurrent Neural Network for Daily Forecast of
PM10 Concentrations in Brunei Darussalam
Effa Nabilla Aziza,*, Asem Kasema, Wida Susanty Haji Suhailia, Peijiang Zhaob
School of Computing and Informatics, Universiti Teknologi Brunei, BE1410, Brunei Darussalam
Big Data Analytics Laboratory, National Institude of Information & Communications Technology, 184-8795, Tokyo, Japanp20190005@student.utb.edu.bnabPM10 is a particulate matter with an aerodynamic diameter less than or equal to 10 µm. It is one of the primarypollutants contributing to the ambient air quality level. Air quality monitoring in Brunei Darussalam is using only
the PM10 concentrations to measure the nation’s daily Pollutant Standard Index (PSI). This study sheds lighton a data-centric landscape of air pollution prediction in Brunei Darussalam, highlights potential uses offorecasting daily PM10 concentrations, and presents comparisons of prediction models built using several
methods, namely: moving average, linear regression, recurrent neural network (RNN), long short-term
memory (LSTM), LSTM with 1-D convolutions, and convolutional recurrent neural network (CRNN). This study
is using daily PM10 concentrations obtained from the air quality monitoring stations located at every district inBrunei Darussalam for a period of 15 y (2005–2019). The results of the analysis of the daily prediction
performance on shows that the CRNN approach provides the most accurate prediction among comparedmethods. The mean value of RMSE, MAE and SMAPE for the CRNN model are 3.414, 2.293 and 0.125. The
results from the CRNN model can be used as part of early-warning application with the ability to provide healthadvisory such as wearing face masks or limit outdoor activities, or to show safer routes to school or work fromheavy polluted areas, to mitigate the negative impacts of haze pollution on the citizens. Future work would
include multiple days predictions, the inclusion of air quality data from neighbouring countries in Southeast
Asia to account for transboundary air pollution, and the inclusion of other pollutants concentrations: carbon
monoxide (CO), fine particulate matter (PM2.5), nitrogen dioxide (NO2), sulphur dioxide (SO2) and ozone (O3).

1. Introduction
Haze pollution has been a recurrent issue in Southeast Asia region, and the sources may come from localized
or transboundary pollution. Transboundary haze episodes are mostly caused by long-range transport ofbiomass fires from slash-and-burn activities in Indonesia (Dotse et al., 2017) during dry seasons, with theprevailing southern monsoon wind going upwards, affecting several countries in the region, including Brunei
Darussalam, Malaysia, Singapore, Indonesia, and Southern Thailand. In Brunei Darussalam, most of thelocalized haze episodes were resulted from peat fires where the peatlands area is being drained ordeforested. This have released carbon emission that accumulated from dead plant matter, and lead topeatland fires and resulted in smoke haze and greenhouse gases into the atmosphere. Particulate matter(PM10) is the only pollutant used in determining the severity of haze and air quality index in Brunei
Darussalam.
Several studies have shown significant relationship between cardiovascular and respiratory morbidity rate withthe level of PM10 concentrations. Newell et al. (2017) analysed an increase rate of cardiovascular (0.27 %)and respiratory (0.56 %) morbidity in China correlated with the increase of PM10 concentrations in China.
During the catastrophic 1997–1998 haze episodes in Brunei Darussalam, the PM10 concentrations were linked
to the increase cases of respiratory morbidity, such as are: asthma, influenzas, acute upper respiratory
infections, pneumonia, bronchitis, emphysema, and conjunctivitis (Anaman and Ibrahim, 2003), and the dropof visibility (Yadav et al., 2003). Brunei have also lost 3.75 % number of tourists, with an estimation of
economic loss of BND 1 million (Anaman and Looi, 2000) and Brunei Airport was forced to close due to thepoor visibility and most flights were delayed or cancelled (Limin et al., 2006). Anaman (2001) surveyed several
Paper Received: 15/07/2020; Revised: 30/08/2020; Accepted: 01/09/2020
Please cite this article as: Aziz E.N., Kasem A., Haji Suhaili W.S., Zhao P., 2021, Convolution Recurrent Neural Network for Daily Forecast of
PM10 Concentrations in Brunei Darussalam, Chemical Engineering Transactions, 83, 355-360 DOI:10.3303/CET2183060

356

households during the 1998 haze episodes and discovered that the average household spent about BND 15
on face masks to reduce the negative effects of the haze, in which for 65, 000 households in Brunei
Darussalam would have estimation cost of BND 1 million for the daily usage of the face masks alone.
Predictions on PM10 concentrations have been widely studied due to its potential negative impacts on humanwell-being, economic, and biodiversity, and its important role in climate change (IPCC, 2013). This led to many
governments and stakeholders in Brunei Darussalam to monitor PM10 concentration closely and focus
research on PM10 predictions. Many studies conducted on particulate matter have used mathematicalstatistical models (Donkelaar et al., 2010) to make long-term prediction by using climate model or a satellite
remote sensing. Several machine learning methods (Saeed et al., 2017) were used to improve accuracy inshort-term prediction with the inclusion of meteorological data, which can influence the predicted values.
Another potential way to predict particulate matter concentrations is using deep learning techniques, forexample hybrid of convolutional neural network and long short-term memory methods (Yang et al., 2020) wereused to predict hourly particulate matter concentrations in Seoul, South Korea; and to date, prediction method
study on daily PM10 concentration in Brunei is using hybrid framework of genetic algorithm, random forestsand back propagation neural networks (Dotse et al., 2017) with using 5 y of air quality data (2009-2013).
The literature review above identified some research gaps. Firstly, prediction study on PM10 concentration
using hybrid method of convolution neural network and recurrent neural network, and evaluation on
performance models using several neural network-based methods have not been considered in the literature
on data-centric landscape of air pollution prediction in Brunei Darussalam. Prediction methods using recent
data on PM10 concentration in Brunei have also not been used, and the use of recent data is essential
especially in forecasting study as it may improve the accuracy and applicability of the prediction models.
Lastly, there are few studies on negative impacts of haze episodes in Brunei using recent haze episodes, as
most of the studies are based on the catastrophic 1997-1998 haze episodes (Anaman and Ibrahim, 2003) inBrunei. In summary, the main gap in the literature study is a thorough analysis on PM10 concentration
prediction using recent air quality data in Brunei, and comparing the model performance while considering the
spatial and temporal distribution of the data that add significant contribution to the framework.
This study aims to utilize convolution recurrent neural network as proposed in a PM2.5 prediction study inJapan (Zhao and Zettsu, 2018), using longer PM10 dataset of 15 y (2005-2019) provided by the Department ofEnvironment, Park and Recreation (JASTRE), gathered from four stations in every district, to predict the daily
PM10 concentrations in Brunei Darussalam. This study also conducted other prediction methods such as linear
regression, recurrent neural network, and long short-term memory, and evaluate all methods using
performance metrics. Results from the prediction model can aid the governments and organizations inmonitoring the air quality, acts as early warning advisory to the public. A summarized data in Table 1
highlights the total number of days with PM10 exceeding 50 μgm-3 (above the level for good air quality
guidelines issued by the Ministry of Health in 2013 health advisory). Table 1 shows that 2013 and 2015 had
the highest number of exceedance days (with a total of 36 d in 2013 and 74 da in 2015).
Table 1: Number of days PM10 concentrations exceeded 50 μgm-3 (above MOH guideline for good air quality)
District
Year
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total

Anggerek
Brunei-Muara
7
8
2
8
4
3
13
13
58

Bukit Bendera
Tutong
1
1
13
5
7
7
26
3
1
14
78

Mumong
Belait
6
20
3
2
20
2
8
5
21
6
32
6
1
25
157

Taman Batang Duri
Temburong
1
20
12
3
5
3
3
47

Sum of Number of Days
Exceeding 50 μgm-3
15
48
3
5
53
2
20
12
36
6
74
9
2
55

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The contribution and novelty of this study to the research literature are:
1. Utilize the new proposed prediction method that combines both convolution and recurrent neuralnetwork (Zhao and Zettsu, 2018) with the inclusion of the spatial and temporal distribution of thedataset, to predict daily PM10 concentrations.
2. All prediction methods used in this study can be used as real-time daily forecasting, notably convolution
recurrent neural network model, reducing the expense of using tapered element oscillating microbalance
instrument to monitor the pollutant concentration.
3. This prediction method is using larger dataset of air quality concentrations from Brunei (2005-2019),
being the first prediction study to develop prediction methods using 15 y of recent air quality dataset.

2. Data and methods
2.1 Study area and data
Brunei Darussalam (latitude 4.5353° N, longitude 114.7277° E) and its four administrative districts are shownin Figure 1, indicating the locations of Anggerek air quality monitoring station in Brunei Muara district (latitude
4.9329° N, longitude 114.9415 E), Bukit Bendera air quality monitoring station in Tutong district (latitude
4.8102° N, longitude 114.6601° E), Mumong air quality monitoring station in Belait district (latitude 4.5751° N,
longitude 114.2330° E) and Taman Batang Duri air quality monitoring station in Temburong district (latitude
4.5786° N, longitude 115.1215° E). The PM10 data used in this study is gathered from four air quality
monitoring stations for every district and provided by JASTRE.

Figure 1: Map showing the locations of the air quality monitoring stations in each district in Brunei Darussalam.
The mean daily PM10 concentrations and meteorological datasets were pre-processed and restructured to thefollowing format: year, month, day, latitude, and longitude of the air quality monitoring station, and the mean
daily PM10 concentration of the station. The available data was divided into two subsets of training and test
sets. 13 y data (2005–2017) were used for training the models, and 2 y data (2018–2019) were used fortesting the trained models.
2.2 Methods
This study experimented with several methods to forecast PM10, including common mathematical methods,
and Neural Networks–based methods, and also leveraged an approach of utilizing Convolutional Recurrent
Neural Network (CRNN) as proposed by Zhao and Zettsu (2018). A naïve forecast is used as baseline model,
in which the previous timestep (i.e. day) of PM10 values are used as the next-day forecasts, without any
attempt to model the data. The predicted results by naïve forecast are used to compare with the forecast
results by other models. Moving average forecast is formed by taking the average value of PM10 over anumber of previous timesteps, and the averaging window is then shifted forward throughout the PM10 data.
Linear regression is also used by estimating a linear function of the parameters in a previous time window ofPM10 data (e.g. window size is 7 d). Recurrent neural network (RNN) models were also used, including simple
RNN, bidirectional long short-term memory (LSTM), and LSTM with 1-D convolutional layer. The methods
were applied on each station’s data individually as multiple time-series prediction tasks. The approach
proposed by Zhao and Zettsu (2018) allows to combine spatio-temporal data from multiple locations and358

model it by using CRNN model. Figure 2 shows an example of how convolutional networks process the spatial
features of the data.

Figure 2: The processes of feeding spatial environment data based on coordinates to the convolutional neuralnetwork to generate a spatial feature map.
Missing data within the grid, i.e. where there are no stations, are approximated using inverse distanceweighting (IDW) for its simplicity. Other methods that take terrain into account (Wang et al., 2019) may be also
used.
2.3 Model performance evaluation
Performance evaluation is used to calculate the error between the predicted and the real observed data on thetest set, which can be measured using different types of performance metrics. In this study, the following
metrics were used: mean absolute error (MAE), root mean squared error (RMSE), and symmetric mean
absolute percentage error (SMAPE).
MAE measures the average of absolute differences between predicted data and the real observed data. MAE
value close to zero suggests both predicted and observed data has good agreement, as shown in Eq(1):1

𝑀𝑀𝑀𝑀𝑀𝑀 = 𝑛𝑛 ∑𝑛𝑛𝑖𝑖=1�𝑦𝑦𝑝𝑝 − 𝑦𝑦𝑜𝑜 �(1)

RMSE calculates the standard deviation of predicted data from the real observed data, as shown in Eq(2):∑𝑛𝑛 �𝑦𝑦𝑝𝑝 − 𝑦𝑦𝑜𝑜 �

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = � 𝑖𝑖=1

𝑛𝑛

2

(2)

SMAPE calculates the absolute differences between predicted data and the real observed data and square
the result, as shown in Eq(3):𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 =

100 %
𝑛𝑛

�𝑦𝑦 − 𝑦𝑦 �∑𝑛𝑛𝑖𝑖=1 |𝑦𝑦 𝑝𝑝|+ 𝑜𝑜𝑜𝑜� 𝑦𝑦𝑝𝑝 �(3)

where, in Eq(1) to Eq(3), yp is the predicted data, yo is the real observed data, and n is the total number ofsamples.

3. Results and Discussions
The computational experiments for this study were conducted using Python and TensorFlow. The parameters
for the predictive models were learnt by training using the 15 y dataset (2005–2017) of mean daily PM10
concentrations (80 % of the data), and the remaining 20 % of the data (2018–2019) were used for evaluation
to avoid biased optimistic evaluation due to over-fitting.
For Moving Average method, it was found that a two-days averaging window gave the best performance on
the test set among the few experimented sizes (2-10). For the other methods, model fitting was run
individually on each station’s training dataset, using a variety of parameters. All experiments, except for
CRNN, used 500 epochs (very similar results were obtained with 80 and 300 epochs as well). For the CRNN
method, the CNN part ran for 800 epochs and the RNN part ran for 700 epochs.
The simple RNN network used two layers of 40 neurons each; the LSTM network used two bidirectional layersof 32 LSTM units each, and the LSTM with 1D-Conv used two unidirectional layers of 32 LSTM units,
preceded with a Conv1D layer of 32 units (kernel size is 5).
Table 2 presents the performance metrics of each method, averaged for all stations, with performance resultsof the CRNN method showing the least error in prediction, across all metrics.

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Table 2: Mean performance metrics of the models at the four stations
MetricEpochNaïve Forecast
Moving Average
Linear Regression
Simple RNN
LSTM
LSTM 1D-Conv
CRNN

RMSE
80
5.294
5.867
5.217
5.419
5.393
5.575
3.414

300

500

5.180
5.168
5.353
5.784

5.169
5.195
5.270
5.917

MAE
80
3.241
3.662
3.245
3.471
3.293
3.386
2.293

300

500

3.226
3.228
3.349
3.494

3.216
3.228
3.330
3.548

SMAPE
80
0.156
0.172
0.167
0.164
0.155
0.160
0.125

300

500

0.155
0.155
0.160
0.165

0.154
0.155
0.159
0.165

Figure 2 shows an example of how one model has fit the training data and is also able to generalize andpredict the data in the test set for the station in Belait district.
(a)

(b)

Figure 3: Example of time-series graph of the observed and predicted values of PM10 concentrations at
Mumong station, Belait district, for (a) testing sets (2018-2019), and (b) training sets from 2005 to 2017.
Overall, based on the obtained metrics from all methods, it seems that all models have a relatively small error
for one-day prediction, this finding suggests that probably any of them can be used in real forecast. It is alsointeresting to note that most of the methods, except for CRNN, achieved a slightly worse error than the naïve
forecasting. For practical applications, it is believed that multi-days forecast is necessary, and the accuracy of
prediction will degrade much more compared to only single future timestep case, and it is expected that the
CRNN method will prevail as the suitable method to be used.

4. Conclusions
This study conducted several prediction methods to forecast daily PM10 concentrations in four districts ofBrunei Darussalam and evaluated the performance of these methods using Mean Absolute Error (MAE), Root
Mean Squared Error (RMSE), and Symmetric Mean Absolute Percentage Error (SMAPE) metrics. Convolution
Recurrent Neural Network (CRNN) model has shown the most satisfactory forecasting results across all
metrics, with MAE, RMSE, and SMAPE values of 3.414, 2.293, and 0.125. Since air pollution is a recurrent

360

issue in Brunei Darussalam especially during the dry seasons, the results from the forecasting model can beused as part of an early-warning application with provides health advisory for citizens, such as wearing
facemasks or limiting outdoor activities, or to show safer routes to school or work from heavily polluted areas,
and to mitigate the negative impacts of haze pollution on the residents. This application aids the government,
especially the Ministry of Health, as PM10 concentrations have been shown to have negative impacts onhuman well-being and many studies have shown significant relationship between PM10 concentrations withcardiovascular and respiratory diseases. Future work should include running the CRNN model for multitimesteps predictions, and the use of air quality data from neighbouring countries in Southeast Asia region toaccount for transboundary air pollution prediction. One possible approach similar to what has been conductedby Zhao and Zettsu (2019) is “Convolution Recurrent Neural Networks Based Dynamic Transboundary Air
Pollution Prediction”. Besides, the inclusion of other meteorological parameters such as rainfall, humidity, andtemperature, and other pollutants concentrations, such as carbon monoxide (CO), fine particulate matter(PM2.5), nitrogen dioxide (NO2), sulphur dioxide (SO2) and ozone (O3) is expected to improve the accuracy of
predictions, and the applicability of its results.
Acknowledgments
The authors acknowledge the support from JASTIP-Net 2019 Grant, and Universiti Teknologi Brunei's Internal
Grant "UTB/GSR/1/2019(10)" and thank them for supporting the research activities required to complete thisstudy. A great appreciation is extended to the Department of Environment, Park and Recreation (JASTRE) for
providing the daily PM10 concentrations data.
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