EGU22-8225, updated on 25 Apr 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Spatial Ground-Level Particulate Matter (PM10) in Indonesia using Machine Learning

Donaldi S Permana1, Yuaning Fajariana1, Kharisma Aprilina1, Danang E Nuryanto1, Utoyo A Linarka1, Andersen Panjaitan1, Nelly F Riama1, Ardhasena Sopaheluwakan1, M Rizky Munggaran2, and Dwikorita Karnawati1
Donaldi S Permana et al.
  • 1Indonesia Agency for Meteorology Climatology and Geophysics (BMKG), Jakarta Pusat, Indonesia (
  • 2Bandung Institute of Technology

Large-scale forest fires often occur in Indonesia and affect air quality and human health. The effect of forest fire on air quality quantified by rising PM10 concentration on Indonesia Meteorological, Climatological and Geophysical Agency (BMKG) observation network. A few PM10 observation networks and uneven distribution in Indonesia make it difficult to present spatial ground-level PM10.  The aim of this study was to estimate ground-level PM10 in Indonesia and present the spatial distribution of ground-level PM10 using machine learning. Support Vector Regression (SVR) techniques were used to estimate the PM10 content from heterogeneous data sources, including ground measurements provided by BMKG, numerical model data, and hotspot retrieved from NASA/LANCE – FIRMS for satellite imagery. RMSE and MSE were used to evaluate the estimation result. We also present the modeling framework on the forecast of the CAMS Copernicus model in Indonesia. The performance of various input parameter configurations of SVR for estimating the ground-level PM10 as indicated by low prediction errors.

How to cite: Permana, D. S., Fajariana, Y., Aprilina, K., Nuryanto, D. E., Linarka, U. A., Panjaitan, A., Riama, N. F., Sopaheluwakan, A., Munggaran, M. R., and Karnawati, D.: Spatial Ground-Level Particulate Matter (PM10) in Indonesia using Machine Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8225,, 2022.