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p-Index From 2021 - 2026
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All Journal Science and Technology Indonesia
Angga Yolanda Putra
Directorate of Laboratory Management, Research Facilities, and Science and Technology Park, National Research and Innovation Agency, Jakarta, 10340, Indonesia
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Environmental Science Materials Science & Nanotechnology Physics

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Synergistic Integration of Multi-Sensor Satellite Data and Gradient Boosting Machine Learning for High-Resolution PM₂.₅ Estimation during Tropical Peatland Fires Dessy Gusnita; Iis Sofiati; Fadhlullah Ramadhani; Angga Yolanda Putra; Risyanto; Waluyo Eko Cahyono; Tatik Kartika; Muhammad Priyatna; Estiningtyas Kusumastuti
Science and Technology Indonesia Vol. 11 No. 3 (2026): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2026.11.3.1089-1105

Abstract

Tropical peatland and forest fires are critical contributors to regional haze and public health crises, yet ground-based monitoring in these regions remains sparse. This study develops a robust framework for estimating surface PM₂.₅ concentrations during extreme fire events (2021–2025) by integrating multi-sensor satellite observations with in-situ data through a Hist Gradient Boosting Regressor (HGBR) approach. To enhance predictive accuracy, we implemented advanced feature engineering, including 1–3 days of exogenous lags, rolling statistics (3 and 7-day windows), and aerosol–meteorological interaction variables. Our analysis of multiple pollutants (PM₂.₅, NO₂, SO₂, CO, HC, and O₃) reveals that during active fire periods, the Air Quality Index (AQI) frequently escalated to "Unhealthy" and "Hazardous" levels. The proposed HGBR model demonstrated high fidelity in representing spatiotemporal variability, achieving a coefficient of determination (R² = 0.72), with an RMSE of 14.26 μg/m³ and MAE of 8.49 μg/m³ (n = 339). These results validate the efficacy of machine learning-driven satellite monitoring in bypassing the limitations of fragmented ground station networks. This framework offers a scalable solution for operational air quality forecasting and early warning systems in fire-prone equatorial regions.
Co-Authors Dessy Gusnita Estiningtyas Kusumastuti Fadhlullah Ramadhani Iis Sofiati Muhammad Priyatna Risyanto Tatik Kartika Waluyo Eko Cahyono