Hacettepe Journal of Mathematics and Statistics 2651-477X 2651-477X Hacettepe University 10.15672/hujms.1849711 Statistical Data Science Spatial Statistics İstatistiksel Veri Bilimi Mekansal İstatistik Hybrid conditional autoregressive generalized random forest for overdispersed spatial counts: Evidence from Kalimantan wildfire hotspots https://orcid.org/0009-0003-2541-1666 Azis İrfani IPB University https://orcid.org/0000-0002-3163-4343 Djuraidah Anik IPB University https://orcid.org/0000-0001-7971-3123 Aidi Muhammad Nur IPB University https://orcid.org/0000-0002-0800-591X - Indahwati IPB University https://orcid.org/0000-0002-4532-854X Sopaheluwakan Ardhasena Meteorological, Climatological, and Geophysical Agency (BMKG) 03 09 2026 55 2 734 751 12 30 2025 02 21 2026 Copyright © 2002, Hacettepe Journal of Mathematics and Statistics 2002 Hacettepe Journal of Mathematics and Statistics

This paper proposes a negative binomial conditional autoregressive generalized random forest model for overdispersed spatial count data. The framework combines a flexible nonparametric mean function estimated via generalized random forests with conditional autoregressive spatial random effects under a negative binomial likelihood. We evaluate the proposed method through a simulation study and an empirical application to satellitederived wildfire hotspot counts in Kalimantan, Indonesia (September 2024), using rainfall, air temperature, relative humidity, wind speed, and the number of rainless days as covariates. Across simulation scenarios, negative binomial conditional autoregressive generalized random forest yields lower prediction errors than the benchmark negative binomial conditional autoregressive random forest. In the Kalimantan application, the model achieves a root mean squared error of 3.64 and a mean absolute error of 2.11 hotspots per grid cell. Variable importance analysis indicates that air temperature is the most influential predictor of the spatial distribution of hotspots.

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