Hybrid Machine Learning for Forecasting and Monitoring Air Pollution in Surabaya

Suhartono, Achmad Choiruddin*, Hendri Prabowo, Muhammad Hisyam Lee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


This research aims to propose hybrid machine learnings for forecasting and monitoring air pollution in Surabaya. In particular, we introduce two hybrid machine learnings, i.e. hybrid Time Series Regression – Feedforward Neural Network (TSR-FFNN) and hybrid Time Series Regression – Long Short-Term Memory (TSR-LSTM). TSR is used to capture linear patterns from data, whereas FFNN or LSTM is used to capture non-linear patterns. Fifteen half-hourly series data, i.e. CO, NO2, O3, PM10, and SO2 in three SUF stations at Surabaya, are used as the case study. We compare the forecasting accuracy of these hybrid machine learnings with several individual methods (i.e. TSR, ARIMA, FFNN, and LSTM), and combined methods (i.e. TSR with AR error and TSR with ARMA error). The identification step showed that these air pollution data have double seasonal patterns, i.e. daily and weekly seasonality. The comparison results showed that no superior method that yields the most accurate forecast for all series data. Moreover, the results showed that hybrid methods gave more accurate forecast at 8 series data, whereas the individual methods yielded better results at 7 series data. It supported that methods that are more complex do not always produce better forecasts than simple methods, as shown by the first result of the M3 competition. Additionally, the results of the forecast of air pollution index for monitoring air pollution in Surabaya show that the air quality is in good and moderate air pollution levels for duration of 19.30 to 03.00 and 0.30 to 19.30, respectively.

Original languageEnglish
Title of host publicationSoft Computing in Data Science - 6th International Conference, SCDS 2021, Proceedings
EditorsAzlinah Mohamed, Bee Wah Yap, Jasni Mohamad Zain, Michael W. Berry
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9789811673337
Publication statusPublished - 2021
Event6th International Conference on Soft Computing in Data Science, SCDS 2021 - Virtual, Online
Duration: 2 Nov 20213 Nov 2021

Publication series

NameCommunications in Computer and Information Science
Volume1489 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference6th International Conference on Soft Computing in Data Science, SCDS 2021
CityVirtual, Online


  • Air pollution
  • Forecasting
  • Hybrid
  • Machine learning
  • Monitoring


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