Identifying Leading Hazards in Riau Islands: A Monthly Markov Chain Analysis of Disaster Dominance Patterns - Nahrul Hayati; Eko Sulistyono; Andini Setyo Anggraeni; Vitri Aprilla Handayani; Sabarinsyah; Laras Devikaduri (Jurnal Matematika UNAND, 2026)
This study analyzes disaster dominance patterns in the Riau Islands using a monthly Markov chain model with five states: non hazard (S0), hydrological (S1,flood), geomorphological (S2,landslide), meteorological (S3,extreme weather), and ecological (S4,wildfire) hazard. Based on 2019-2024 data from Indonesia’s National Disaster Management Agency (BNPB), the research quantifies transition probabilities between hazard states and computes steady-state distributions to identify long-term risks. Key findings reveal wildfires dominate the system with 40.6% steady-state probability and high persistence (63% monthly recurrence), reflecting the region’s dry-seasonal vulnerability. Extreme weather and floods show significant but secondary prevalence (24.1% and 12.5%, respectively). Landslides are rare (2.5%) but often escalate to wildfires. The transition matrix highlights wildfire transitions following floods (44.5% probability), suggesting delayed risk cascades. Methodologically, this study advances archipelagic hazard modeling by integrating monthly timesteps and hazard taxonomy, offering granular insights for policymakers. Practical implications include prioritizing peatland restoration, flood-resistant infrastructure, and ASEAN-wide early warning systems to address transboundary haze.