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Dedi Darwis
Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Spatio-Temporal Forest and Land Fire Risk Modeling in Sumatra Using Atmospheric-Edaphic Integration via Bivariate Fuzzy C-Means Ade Firmansyah; Dedi Darwis
International Journal of Advances in Data and Information Systems Vol. 7 No. 2 (2026): August 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i2.1564

Abstract

The study focused on the persistent forest and land fires in Sumatra and the conventional early warning systems that showed limitations in capturing the dynamics of the soil-atmosphere and tended to overestimate risk zones during the peak periods of fires. Existing multivariate systems also showed limitations when there was a lack of integration between atmospheric water demand and edaphic (soil) vulnerability, resulting in lower spatial efficiency in the risk area. This paper presented a new bivariate physics-based model called the VPD-LVI Integrated Fuzzy (VLIF) Model, which integrated Vapor Pressure Deficit (VPD) and Land Vulnerability Index (LVI). For the VLIF-Model, to ensure a high level of spatial detail, the model processed 2,614,056 spatiotemporal observations, representing a grid resolution of 0.1° over a two-year period (2023–2024). The model showed acceptable structural stability at k=3 (FPC = 0.6137), with distinct risk zoning contrasts. Furthermore, validation against 61,015 VIIRS thermal anomalies indicated the model's predictive reliability with an ROC-AUC of 0.8288 and a Brier Score of 0.0169, suggesting good discrimination and calibration. In the spatial efficiency analysis, 17.80% of the area categorized as 'high' risk captured 62.28% of actual thermal anomaly grids, achieving a lift factor of 3.50 and a recall of 88.91%. The VLIF-Model provided clearer contouring of peak fire zones and enabled a more targeted spatial risk intelligence layer for specific tropical peatland mitigation efforts.
Co-Authors Ade Firmansyah